In the context of the Home Credit Default Risk (HCDR) Kaggle Competition, the objective is to develop a robust predictive model to determine whether a client will successfully repay a loan. Home Credit, a leading financial institution, aims to ensure a positive loan experience for individuals who face challenges in securing loans due to limited or nonexistent credit histories. To achieve this goal, Home Credit leverages a diverse set of alternative data sources, including telco and transactional information, to assess their clients' repayment capabilities.
| Phase | Contributor | Contribution Details |
|---|---|---|
| Phase 1 | Naveen | Phase Leader |
| Phase 1 | Naveen | Data files overview |
| Phase 1 | Anurag | Describing the data |
| Phase 1 | Bharath | Planning credit assignment |
| Phase 1 | Alexis | Git Repo Creation |
| Phase 1 | Anurag | Metrics description |
| Phase 1 | Bharath | ML Algorithms to be used |
| Phase 1 | Naveen | Pipeline description |
| Phase 1 | Alexis | Block model of Pipeline |
| Phase 1 | Bharath | Gantt Chart preparation |
| Phase 1 | Naveen | Submission of Phase 1 |
| Specific | Measurable | Achievable | Relevant | Time-bound | Responsible |
|---|---|---|---|---|---|
| Provide concise descriptions of key data files in HCDR | Include essential details for each file, ensuring clarity in understanding | Summarize the essential information for each file accurately | Offer relevant details pertinent to data analysis | 5 | Naveen |
| Provide a comprehensive and clear description of the dataset, outlining its key features, variables, and structure | Cover all relevant aspects of the dataset, ensuring no crucial information is omitted | Deliver a detailed yet concise overview | Focus on describing data elements essential for analysis | 5 | Anurag |
| Clearly define the metrics used for evaluating model performance, specifying each metric's purpose and calculation method | Include all relevant evaluation metrics | Provide a detailed description of each metric without overwhelming the reader, balancing depth with clarity | Focus on metrics directly impacting the project's goals, emphasizing their significance in assessing model accuracy and effectiveness | 5 | Anurag |
| Specify the machine learning algorithms to be utilized, including their names and brief descriptions of their functionalities | List all selected algorithms, ensuring a comprehensive overview of the diverse techniques chosen for the project | Include algorithms feasible for the project scope and dataset, ensuring practicality and relevance | Focus on algorithms tailored to address the project's goals, emphasizing their suitability for the specific prediction task | 10 | Bharath |
| Outline the steps of the machine learning pipeline, detailing data preprocessing, feature engineering, model selection, and evaluation methods | Clearly define each stage of the pipeline, ensuring a complete and coherent overview of the entire process | Provide a comprehensive yet concise description, offering a clear understanding of the workflow without unnecessary complexity | Focus on the pipeline elements crucial for model development, emphasizing their direct impact on achieving project objectives | 5 | Naveen |
| Develop a clear and concise block model and git repo creation | Include labeled blocks representing each stage, ensuring a visually comprehensive overview of the pipeline's structure | Design an intuitive and easy-to-understand block model, focusing on simplicity and coherence for effective communication of the pipeline workflow | Highlight critical stages in the pipeline, ensuring the visual representation aligns with the project's specific objectives and analysis requirements | 10 | Alexis |
| Phase | Contributor | Contribution Details |
|---|---|---|
| 2 | Bharath | Phase Leader |
| 2 | Alexis, Bharath | Exploratory Data Analysis |
| 2 | Anurag | Pipeline Coding |
| 2 | Naveen | Running Experimental Pipelines |
| 2 | Naveen | Planning Credit Assignment |
| 2 | Naveen | Create Presentation |
| 2 | Alexis | Making Comparison |
| 2 | Anurag | Making notes of results from Experiments |
| 2 | Bharath | Decide Slides for presentation |
| 2 | Naveen | Gantt chart Preparation |
| 2 | Bharath | Submission for phase 2 |
| Specific Task | Measurable Outcome | Achievable Goals | Relevant Information | Time-bound | Responsible |
|---|---|---|---|---|---|
| Exploratory Data Analysis | Thorough exploration of dataset with key insights | Identify patterns, outliers, and trends in the data | Essential for understanding data characteristics | 7.5 | Alexis, Bharath |
| Pipeline Coding | Successful implementation of the data pipeline | Code functionality and structure | Foundation for automated data processing | 7.5 | Anurag |
| Running Experimental Pipelines | Executed pipelines with reproducible results | Validate pipeline functionality with sample data | Essential for testing and refining the pipeline | 4 | Naveen |
| Planning Credit Assignment | Detailed plan for assigning credit in the project | Develop a clear plan for credit assignment | Ensure fair acknowledgment of team contributions | 4 | Naveen |
| Create Presentation | Completed presentation slides for phase 2 | Include key findings, visuals, and insights | Communicate results effectively to stakeholders | 4 | Naveen |
| Making Comparison | Comparative analysis of results from experiments | Identify differences and similarities | Essential for drawing conclusions and insights | 7.5 | Alexis |
| Making Notes of Results from Experiments | Comprehensive documentation of experimental outcomes | Summarize key findings and observations | Essential for future reference and reporting | 7.5 | Anurag |
| Decide Slides for Presentation | Finalized selection of slides for the presentation | Review and choose the most relevant slides | Ensure a coherent and impactful presentation | 5 | Bharath |
| Gantt Chart Preparation | Completed Gantt chart outlining project timelines | Identify key milestones and project duration | Essential for project management and tracking | 4 | Naveen |
| Submission for Phase 2 | Submission of all required deliverables for phase 2 | Compile and organize all necessary documents | Ensure timely completion and project progress | 5 | Bharath |
| Phase | Contributor | Contribution Details |
|---|---|---|
| 3 | Anurag | Phase Leader |
| 3 | Naveen | Planning Credit Assignment |
| 3 | Bharath | Polynomial Feature Expansion |
| 3 | Alexis | Incorporating Domain specific features |
| 3 | Naveen | Exploratory Modelling of the Data |
| 3 | Anurag | Model Training |
| 3 | Anurag | Baseline Modelling with Imbalanced Dataset +Advanced Features |
| 3 | Bharath | Implementing oversampling with smote |
| 3 | Alexis | ML models with domain feature inclusion |
| 3 | Bharath | Hyperparameter tuning |
| 3 | Naveen | Model Performance Comparision |
| 3 | Alexis | Recording Results |
| 3 | Alexis | Syncing the notebook |
| 3 | Anurag | Presentation creation |
| 3 | Bharath | Video preparation |
| 3 | Naveen | Gantt Chat Preparation |
| 3 | Anurag | Submission of Phase 3 |
| Specific Task | Measurable Outcome | Achievable Goals | Relevant Information | Time-bound | Responsible |
|---|---|---|---|---|---|
| Phase Leadership | Successful coordination and guidance during Phase 2 | Provide clear direction and support for team members | Ensure effective teamwork and progress | 7.5 | Anurag |
| Planning Credit Assignment | Detailed plan for assigning credit in the project | Develop a clear plan for credit assignment | Ensure fair acknowledgment of team contributions | 4 | Naveen |
| Polynomial Feature Expansion | Implementation of polynomial features in the model | Integrate polynomial features for improved modeling | Enhance model complexity and predictive power | 7.5 | Bharath |
| Incorporating Domain Specific Features | Integration of domain-specific features in models | Enhance model relevance to the project domain | Improve model performance with domain knowledge | 7.5 | Alexis |
| Exploratory Modeling of the Data | Thorough exploration and initial modeling of data | Identify potential patterns and insights | Lay the groundwork for subsequent model training | 4 | Naveen |
| Model Training | Successful training of machine learning models | Train models with selected data and features | Prepare models for evaluation and testing | 7.5 | Anurag |
| Baseline Modeling with Imbalanced Dataset | Development of baseline models with imbalanced data | Address challenges posed by imbalanced dataset | Establish a baseline for comparison and improvement | 7.5 | Anurag |
| Implementing Oversampling with SMOTE | Integration of SMOTE for oversampling in the models | Address class imbalance through oversampling | Improve model performance on minority class | 7.5 | Bharath |
| ML Models with Domain Feature Inclusion | Creation of models incorporating domain features | Evaluate models with domain-specific information | Enhance model accuracy and relevance | 7.5 | Alexis |
| Hyperparameter Tuning | Optimization of model hyperparameters | Fine-tune models for improved performance | Enhance model efficiency and generalization | 7.5 | Bharath |
| Model Performance Comparison | Comparative analysis of model performance | Evaluate and compare models based on metrics | Identify the most effective model configurations | 7.5 | Naveen |
| Recording Results | Comprehensive documentation of experimental outcomes | Summarize key findings and observations | Essential for future reference and reporting | 7.5 | Alexis |
| Syncing the Notebook | Synchronization of project notebooks and files | Ensure consistency and version control | Facilitate collaboration and troubleshooting | 4 | Alexis |
| Presentation Creation | Development of presentation slides for Phase 2 | Communicate key findings and insights effectively | Ensure a clear and engaging presentation | 7.5 | Anurag |
| Video Preparation | Creation of video content for project presentation | Compile visuals and narration for the video | Enhance communication and project understanding | 7.5 | Bharath |
| Gantt Chart Preparation | Completed Gantt chart outlining project timelines | Identify key milestones and project duration | Essential for project management and tracking | 4 | Naveen |
| Submission of Phase 3 | Submission of all required deliverables for Phase 3 | Compile and organize all necessary documents | Ensure timely completion and project progress | 5 | Anurag |
| Phase | Contributor | Contribution Details |
|---|---|---|
| 4 | Alexis | Phase Leader |
| 4 | Naveen | Planning Credit Assignment |
| 4 | Alexis | Data preperation for DeepLearning |
| 4 | Anurag | Single neural network |
| 4 | Bharath | Deep Neural Network |
| 4 | Naveen | Define a Loss Function |
| 4 | Bharath | Building the Model and Training the model |
| 4 | Bharath | Video presentation Planning |
| 4 | Alexis | Creating final Repo on Github |
| 4 | Anurag | Working on Final NoteBook |
| 4 | Anurag | Presentation Creation |
| 4 | Alexis | Video Presentation |
| 4 | Naveen | Gantt Chart Preparation |
| 4 | Bharath | Submission of Phase 4 |
| Specific Task | Measurable Outcome | Achievable Goals | Relevant Information | Time-bound | Responsible |
|---|---|---|---|---|---|
| Phase Leadership | Successful coordination and guidance during Phase 4 | Provide clear direction and support for team members | Ensure effective teamwork and progress | 7.5 | Alexis |
| Planning Credit Assignment | Detailed plan for assigning credit in the project | Develop a clear plan for credit assignment | Ensure fair acknowledgment of team contributions | 4 | Naveen |
| Data Preparation for Deep Learning | Well-prepared data for deep learning model | Clean, preprocess, and organize data for modeling | Facilitate effective training of deep learning models | 7.5 | Alexis |
| Single Neural Network | Implementation and training of a single neural network | Develop and train a neural network model | Establish a baseline for more complex models | 7.5 | Anurag |
| Deep Neural Network | Design and training of a deep neural network model | Develop a deep learning model for improved performance | Enhance model complexity and predictive power | 7.5 | Bharath |
| Define a Loss Function | Clear definition of a loss function for model optimization | Establish criteria for model training and evaluation | Enhance model training efficiency | 4 | Naveen |
| Building the Model and Training the Model | Successful construction and training of the model | Implement the designed model and train it | Prepare models for evaluation and testing | 7.5 | Bharath |
| Video Presentation Planning | Detailed plan for creating a video presentation | Outline content, visuals, and narration for the video | Ensure a clear and engaging video presentation | 7.5 | Bharath |
| Creating Final Repository on GitHub | Establishment of the final project repository on GitHub | Create a well-organized and documented repository | Facilitate collaboration and version control | 7.5 | Alexis |
| Working on Final Notebook | Compilation and documentation of final project results | Summarize key findings and observations | Essential for future reference and reporting | 7.5 | Anurag |
| Presentation Creation | Development of presentation slides for Phase 4 | Communicate key findings and insights effectively | Ensure a clear and engaging presentation | 7.5 | Anurag |
| Video Presentation | Creation of video content for Phase 4 presentation | Compile visuals and narration for the video | Enhance communication and project understanding | 7.5 | Alexis |
| Gantt Chart Preparation | Completed Gantt chart outlining Phase 4 timelines | Identify key milestones and project duration | Essential for project management and tracking | 4 | Naveen |
| Submission of Phase 4 | Submission of all required deliverables for Phase 4 | Compile and organize all necessary documents | Ensure timely completion and project progress | 5 | Bharath |
Feel free to adapt the details based on your project's specific needs!
Situation: Home Credit, a leading financial institution, aims to provide improved credit decisions for individuals with limited credit histories. Traditional credit scoring methods often fail to adequately measure a person’s appropriateness for credit, affecting financial inclusion.
Task: Our goal is to develop an accurate predictive model that leverages telcos and other data sources. By participating in the Home Loan Predetermined Risk Project, we strive to create innovative solutions to bridge the gap in credit assessment, and ensure fair and accessible lending to a wider audience.
Action: Using advanced machine learning algorithms and data analytics techniques, we analyze a variety of data types, including applicants’ financial and personal information. Through rigorous feature engineering, model selection, and validation, we build accurate predictive models that distinguish customers’ ability to pay.
Results: The program provides a robust predictive model, empowering home lenders to make informed lending decisions. This model provides a nuanced understanding of applicant credit, promotes financial inclusion and reinforces Home Credit’s mission to provide a positive lending experience for all customers.
Timeframe: Within the scheduled timeline, we actively analyze data, replicate models and deliver credible solutions. Our approach aligns with the competition’s objectives, ensuring timely delivery of outstanding results.
The dataset for the Home Credit Default Risk project encompasses a rich and diverse collection of financial and personal information about loan applicants. Comprising multiple CSV files, it provides a comprehensive view of borrowers' credit histories and behaviors. The primary application_train.csv and application_test.csv files offer vital insights into applicants' demographic details, such as age, income, education, and family status.
Supplementary files like bureau.csv and previous_application.csv extend the dataset, offering historical data from credit bureaus and past loan applications, respectively. POS_CASH_balance.csv, credit_card_balance.csv, and installments_payments.csv files provide intricate details about applicants' previous loans, including payment histories and installment schedules.
In addition to these core files, telco and transactional data further enrich the dataset. The bureau_balance.csv file provides monthly updates on credits in the applicant's credit bureau accounts, adding granularity to the historical data. The dataset's complexity and depth empower data scientists to conduct in-depth analyses and construct predictive models.
This dataset is a valuable resource for machine learning practitioners, enabling the development of accurate credit risk assessment models. Its multidimensional nature allows for sophisticated feature engineering and exploration, contributing significantly to the competition's aim of enhancing lending decisions and promoting financial inclusion.
The seven different sources of data for the Home Credit Default Risk project:
application_train/applicationtest(application{train|test}.csv) (307k rows and 48k rows):
Main training and testing data containing loan application details at Home Credit. Each loan is represented by a unique row identified by the feature SK_ID_CURR. Includes the TARGET variable indicating 0 for repaid loans and 1 for loans with payment difficulties. bureau (1.7 Million rows):
Data on client's previous credits from other financial institutions(bureau.csv) Each previous credit has a row; one loan in application data can have multiple previous credits. bureau_balance (27 Million rows):
Monthly data on previous credits in bureau, with each row representing a month of a previous credit(bureau_balance.csv) Multiple rows for a single previous credit, indicating credit activity over several months. previous_application (1.6 Million rows):
Records previous loan applications at Home Credit for clients with loans in application data(POS_CASH_balance.csv) Each previous application is represented by a single row identified by SK_ID_PREV. POS_CASH_BALANCE (10 Million rows):
Monthly data on previous point-of-sale or cash loans clients had with Home Credit(credit_card_balance.csv) Each row represents one month of a previous point-of-sale or cash loan, allowing tracking of payment behavior. credit_card_balance (Millions of rows):
Monthly data on previous credit card accounts clients held with Home Credit(installments_payments.csv) Each row indicates one month of credit card balance, offering insights into credit utilization and payment patterns. installments_payment (13.6 Million rows):
Payment history for previous loans at Home Credit, capturing both made and missed payments(HomeCredit_columns_description.csv) Each payment, successful or missed, is represented by a row, providing a detailed record of borrower behavior.
These diverse data sources form the foundation for creating predictive models, allowing in-depth analysis of applicants' credit histories and behaviors. The extensive dataset enables the exploration of various features, contributing to accurate credit risk assessment and enhanced lending decisions.
Logistic Regression:
Description: Logistic Regression is a linear algorithm used for binary classification tasks. It models the probability that an instance belongs to a particular class.
Why ?: Suitable for its simplicity and interpretability. It serves as a baseline model and works well when the relationship between features and the target variable is approximately linear.
Random Forest:
Description: Random Forest is an ensemble method that builds multiple decision trees and merges their predictions. It handles non-linearity, captures complex relationships, and reduces overfitting.
Why ?: Suitable for capturing intricate patterns in the data. Random Forest is robust, performs well on large datasets, and handles both numerical and categorical features effectively.
Neural Networks (Deep Learning):
Description: Neural Networks consist of interconnected nodes (neurons) organized in layers. Deep Learning involves neural networks with multiple hidden layers.
Why ?: Suitable for capturing complex, non-linear relationships in the data. Deep Learning excels in tasks where features are highly abstract or hierarchical, potentially capturing nuanced patterns in credit default behavior.
Gradient Boosting Machines (GBM):
Description: GBM builds multiple decision trees sequentially, correcting errors made by previous models. It combines weak learners to create a strong predictive model.
Why ?: Suitable for improving accuracy and capturing complex patterns. GBM excels in reducing bias and variance, making it powerful for predicting credit default risk.
XGBoost (Extreme Gradient Boosting):
Description: XGBoost is an optimized implementation of gradient boosting, designed for speed and performance. It uses regularization techniques to prevent overfitting.
Why ?: Suitable for large datasets and high-dimensional feature spaces. XGBoost handles missing data efficiently and is known for its high predictive accuracy.
Bagging (Bootstrap Aggregating):
Description: Bagging is an ensemble learning method that builds multiple models on different subsets of the training data, using bootstrap sampling. It aims to reduce overfitting and improve model stability by combining diverse predictions.
Why?: Effective for high-variance models like decision trees, bagging averages or votes on multiple models, providing a more robust and generalized prediction.
When evaluating the success of a machine learning model in the Home Credit Default Risk project, it's essential to consider both standard metrics commonly used in classification tasks and domain-specific metrics tailored to the specific objectives of predicting loan defaults. Here's a list of metrics that you might use to measure success:
A confusion matrix is a table used in classification machine learning to evaluate the performance of a model. It presents a summary of the actual vs. predicted classifications done by a classification algorithm. The matrix has four important metrics:
True Positives (TP): The number of instances correctly predicted as positive. True Negatives (TN): The number of instances correctly predicted as negative. False Positives (FP): The number of instances incorrectly predicted as positive (actually negative). False Negatives (FN): The number of instances incorrectly predicted as negative (actually positive).
Accuracy:
Precision:
Recall (Sensitivity):
F1-Score:
ROC-AUC (Receiver Operating Characteristic - Area Under Curve):
PR-AUC (Precision-Recall Area Under Curve):
Profit/Loss Metrics:
Risk Metrics:
Lift and Gain Charts:
Bad Rate Metrics:
Stability Metrics:
We may choose combination of these metrics to finally evaluate our model.
Certainly! Here's a description of the pipeline steps for implementing the machine learning algorithms mentioned earlier in the context of the Home Credit Default Risk project:
These pipeline steps provide a structured approach to implementing the selected machine learning algorithms, ensuring proper preprocessing, feature engineering, model training, evaluation, and optimization for accurate prediction of loan default risk.
Name : Anurag Nampally
Email : anampal@iu.edu
Name : Naveen Rao Vardhieni
Email : nvardhi@iu.edu
Name : Veldi Bharath Sri Vardhan
Email : bhaveldi@iu.edu
Name : Alexis Perez
Email : ap70@iu.edu
Kaggle is a Data Science Competition Platform which shares a lot of datasets. In the past, it was troublesome to submit your result as your have to go through the console in your browser and drag your files there. Now you can interact with Kaggle via the command line. E.g.,
! kaggle competitions files home-credit-default-risk
It is quite easy to setup, it takes me less than 15 minutes to finish a submission.
kaggle.json filekaggle.json in the right placeFor more detailed information on setting the Kaggle API see here and here.
!pip install kaggle
Collecting kaggle
Downloading kaggle-1.5.16.tar.gz (83 kB)
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Building wheels for collected packages: kaggle
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Created wheel for kaggle: filename=kaggle-1.5.16-py3-none-any.whl size=110697 sha256=492f8775a031e452ca103a51f5a617d6ff10ba9a9f20b5345652a24f3f07933b
Stored in directory: c:\users\tanub\appdata\local\pip\cache\wheels\6a\2b\d0\457dd27de499e9423caf738e743c4a3f82886ee6b19f89d5b7
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Installing collected packages: kaggle
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!dir C:\Users\tanub\Downloads
Volume in drive C is Windows-SSD
Volume Serial Number is F43F-BE58
Directory of C:\Users\tanub\Downloads
12/03/2023 06:54 PM <DIR> .
12/03/2023 06:26 PM <DIR> ..
12/03/2023 06:54 PM <DIR> .ipynb_checkpoints
12/02/2023 08:56 PM 1,095,571,496 Anaconda3-2023.09-0-Windows-x86_64.exe
12/01/2023 08:54 AM 1,375,280 ChromeSetup.exe
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11/30/2023 06:21 PM 616,149,608 Docker Desktop Installer.exe
12/02/2023 08:58 PM 22,916,605 FP_GroupN_HCDR_5Phase3_IPYNB.ipynb
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# Copy kaggle.json to the .kaggle directory
!copy C:\Users\tanub\Downloads\kaggle.json C:\Users\tanub\.kaggle
# Remove inherited permissions and grant read permissions to the file
!icacls C:\Users\tanub\.kaggle\kaggle.json /inheritance:r
!icacls C:\Users\tanub\.kaggle\kaggle.json /grant:r "%username%:RW"
1 file(s) copied. processed file: C:\Users\tanub\.kaggle\kaggle.json Successfully processed 1 files; Failed processing 0 files processed file: C:\Users\tanub\.kaggle\kaggle.json Successfully processed 1 files; Failed processing 0 files
!dir C:\Users\tanub\.kaggle
Volume in drive C is Windows-SSD
Volume Serial Number is F43F-BE58
Directory of C:\Users\tanub\.kaggle
12/03/2023 06:57 PM <DIR> .
12/03/2023 06:57 PM <DIR> ..
12/03/2023 06:55 PM 68 kaggle.json
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! kaggle competitions files home-credit-default-risk
name size creationDate ---------------------------------- ----- ------------------- POS_CASH_balance.csv 375MB 2019-12-11 02:55:35 sample_submission.csv 524KB 2019-12-11 02:55:35 HomeCredit_columns_description.csv 37KB 2019-12-11 02:55:35 installments_payments.csv 690MB 2019-12-11 02:55:35 bureau_balance.csv 358MB 2019-12-11 02:55:35 application_test.csv 25MB 2019-12-11 02:55:35 bureau.csv 162MB 2019-12-11 02:55:35 previous_application.csv 386MB 2019-12-11 02:55:35 application_train.csv 158MB 2019-12-11 02:55:35 credit_card_balance.csv 405MB 2019-12-11 02:55:35
Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.
Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.
While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.
Home Credit is a non-banking financial institution, founded in 1997 in the Czech Republic.
The company operates in 14 countries (including United States, Russia, Kazahstan, Belarus, China, India) and focuses on lending primarily to people with little or no credit history which will either not obtain loans or became victims of untrustworthly lenders.
Home Credit group has over 29 million customers, total assests of 21 billions Euro, over 160 millions loans, with the majority in Asia and and almost half of them in China (as of 19-05-2018).
While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.
The HomeCredit_columns_description.csv acts as a data dictioanry.
There are 7 different sources of data:
name [ rows cols] MegaBytes
----------------------- ------------------ -------
application_train : [ 307,511, 122]: 158MB
application_test : [ 48,744, 121]: 25MB
bureau : [ 1,716,428, 17] 162MB
bureau_balance : [ 27,299,925, 3]: 358MB
credit_card_balance : [ 3,840,312, 23] 405MB
installments_payments : [ 13,605,401, 8] 690MB
previous_application : [ 1,670,214, 37] 386MB
POS_CASH_balance : [ 10,001,358, 8] 375MB
Create a base directory:
DATA_DIR = "../../../Data/home-credit-default-risk" #same level as course repo in the data directory
Please download the project data files and data dictionary and unzip them using either of the following approaches:
Download button on the following Data Webpage and unzip the zip file to the BASE_DIRDATA_DIR = r"C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2"
!mkdir $DATA_DIR
A subdirectory or file C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2 already exists.
DATA_DIR
'C:\\Users\\tanub\\Courses\\AML526\\I526_AML_Student\\Assignments\\Unit-Project-Home-Credit-Default-Risk\\Phase2'
!dir $DATA_DIR
Volume in drive C is Windows-SSD
Volume Serial Number is F43F-BE58
Directory of C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2
11/30/2023 07:04 PM <DIR> .
11/30/2023 07:04 PM <DIR> ..
11/30/2023 07:04 PM 3,182,122 HCDR_baseLine_submission_with_numerical_and_cat_features_to_kaggle.ipynb
11/30/2023 07:04 PM 66,899 home_credit.png
11/30/2023 07:04 PM 11 Phase2.md
11/30/2023 07:04 PM 1,368,981 submission.csv
11/30/2023 07:04 PM 1,091,396 submission.png
5 File(s) 5,709,409 bytes
2 Dir(s) 884,921,679,872 bytes free
! kaggle competitions download home-credit-default-risk -p $DATA_DIR
Downloading home-credit-default-risk.zip to C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2
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!dir $DATA_DIR
Volume in drive C is Windows-SSD
Volume Serial Number is F43F-BE58
Directory of C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2
12/03/2023 06:58 PM <DIR> .
11/30/2023 07:04 PM <DIR> ..
11/30/2023 07:04 PM 3,182,122 HCDR_baseLine_submission_with_numerical_and_cat_features_to_kaggle.ipynb
12/11/2019 03:03 AM 721,616,255 home-credit-default-risk.zip
11/30/2023 07:04 PM 66,899 home_credit.png
11/30/2023 07:04 PM 11 Phase2.md
11/30/2023 07:04 PM 1,368,981 submission.csv
11/30/2023 07:04 PM 1,091,396 submission.png
6 File(s) 727,325,664 bytes
2 Dir(s) 884,199,927,808 bytes free
DATA_DIR
'C:\\Users\\tanub\\Courses\\AML526\\I526_AML_Student\\Assignments\\Unit-Project-Home-Credit-Default-Risk\\Phase2'
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
unzippingReq = True #True
if unzippingReq: #please modify this code
zip_ref = zipfile.ZipFile(f'{DATA_DIR}/home-credit-default-risk.zip', 'r')
# extractall(): Extract all members from the archive to the current working directory. path specifies a different directory to extract to
zip_ref.extractall(DATA_DIR)
zip_ref.close()
DATA_DIR = r"C:\Users\tanub\Courses\AML526\I526_AML_Student\Assignments\Unit-Project-Home-Credit-Default-Risk\Phase2\DATA_DIR"
DATA_DIR
'C:\\Users\\tanub\\Courses\\AML526\\I526_AML_Student\\Assignments\\Unit-Project-Home-Credit-Default-Risk\\Phase2\\DATA_DIR'
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
def load_data(in_path, name):
df = pd.read_csv(in_path)
print(f"{name}: shape is {df.shape}")
print(df.info())
display(df.head(5))
return df
datasets={} # lets store the datasets in a dictionary so we can keep track of them easily
ds_name = 'application_train'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)
datasets['application_train'].shape
application_train: shape is (307511, 122) <class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR dtypes: float64(65), int64(41), object(16) memory usage: 286.2+ MB None
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 100006 | 0 | Cash loans | F | N | Y | 0 | 135000.0 | 312682.5 | 29686.5 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 100007 | 0 | Cash loans | M | N | Y | 0 | 121500.0 | 513000.0 | 21865.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 122 columns
(307511, 122)
ds_name = 'application_test'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)
application_test: shape is (48744, 121) <class 'pandas.core.frame.DataFrame'> RangeIndex: 48744 entries, 0 to 48743 Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR dtypes: float64(65), int64(40), object(16) memory usage: 45.0+ MB None
| SK_ID_CURR | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100001 | Cash loans | F | N | Y | 0 | 135000.0 | 568800.0 | 20560.5 | 450000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 100005 | Cash loans | M | N | Y | 0 | 99000.0 | 222768.0 | 17370.0 | 180000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 2 | 100013 | Cash loans | M | Y | Y | 0 | 202500.0 | 663264.0 | 69777.0 | 630000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 |
| 3 | 100028 | Cash loans | F | N | Y | 2 | 315000.0 | 1575000.0 | 49018.5 | 1575000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 4 | 100038 | Cash loans | M | Y | N | 1 | 180000.0 | 625500.0 | 32067.0 | 625500.0 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 121 columns
The application dataset has the most information about the client: Gender, income, family status, education ...
%%time
ds_names = ("application_train", "application_test", "bureau","bureau_balance","credit_card_balance","installments_payments",
"previous_application","POS_CASH_balance")
for ds_name in ds_names:
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)
application_train: shape is (307511, 122) <class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR dtypes: float64(65), int64(41), object(16) memory usage: 286.2+ MB None
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | Cash loans | M | N | Y | 0 | 202500.0 | 406597.5 | 24700.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 100003 | 0 | Cash loans | F | N | N | 0 | 270000.0 | 1293502.5 | 35698.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 100004 | 0 | Revolving loans | M | Y | Y | 0 | 67500.0 | 135000.0 | 6750.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 100006 | 0 | Cash loans | F | N | Y | 0 | 135000.0 | 312682.5 | 29686.5 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 100007 | 0 | Cash loans | M | N | Y | 0 | 121500.0 | 513000.0 | 21865.5 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 122 columns
application_test: shape is (48744, 121) <class 'pandas.core.frame.DataFrame'> RangeIndex: 48744 entries, 0 to 48743 Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR dtypes: float64(65), int64(40), object(16) memory usage: 45.0+ MB None
| SK_ID_CURR | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100001 | Cash loans | F | N | Y | 0 | 135000.0 | 568800.0 | 20560.5 | 450000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 100005 | Cash loans | M | N | Y | 0 | 99000.0 | 222768.0 | 17370.0 | 180000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 2 | 100013 | Cash loans | M | Y | Y | 0 | 202500.0 | 663264.0 | 69777.0 | 630000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 4.0 |
| 3 | 100028 | Cash loans | F | N | Y | 2 | 315000.0 | 1575000.0 | 49018.5 | 1575000.0 | ... | 0 | 0 | 0 | 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 |
| 4 | 100038 | Cash loans | M | Y | N | 1 | 180000.0 | 625500.0 | 32067.0 | 625500.0 | ... | 0 | 0 | 0 | 0 | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 121 columns
bureau: shape is (1716428, 17) <class 'pandas.core.frame.DataFrame'> RangeIndex: 1716428 entries, 0 to 1716427 Data columns (total 17 columns): # Column Dtype --- ------ ----- 0 SK_ID_CURR int64 1 SK_ID_BUREAU int64 2 CREDIT_ACTIVE object 3 CREDIT_CURRENCY object 4 DAYS_CREDIT int64 5 CREDIT_DAY_OVERDUE int64 6 DAYS_CREDIT_ENDDATE float64 7 DAYS_ENDDATE_FACT float64 8 AMT_CREDIT_MAX_OVERDUE float64 9 CNT_CREDIT_PROLONG int64 10 AMT_CREDIT_SUM float64 11 AMT_CREDIT_SUM_DEBT float64 12 AMT_CREDIT_SUM_LIMIT float64 13 AMT_CREDIT_SUM_OVERDUE float64 14 CREDIT_TYPE object 15 DAYS_CREDIT_UPDATE int64 16 AMT_ANNUITY float64 dtypes: float64(8), int64(6), object(3) memory usage: 222.6+ MB None
| SK_ID_CURR | SK_ID_BUREAU | CREDIT_ACTIVE | CREDIT_CURRENCY | DAYS_CREDIT | CREDIT_DAY_OVERDUE | DAYS_CREDIT_ENDDATE | DAYS_ENDDATE_FACT | AMT_CREDIT_MAX_OVERDUE | CNT_CREDIT_PROLONG | AMT_CREDIT_SUM | AMT_CREDIT_SUM_DEBT | AMT_CREDIT_SUM_LIMIT | AMT_CREDIT_SUM_OVERDUE | CREDIT_TYPE | DAYS_CREDIT_UPDATE | AMT_ANNUITY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 215354 | 5714462 | Closed | currency 1 | -497 | 0 | -153.0 | -153.0 | NaN | 0 | 91323.0 | 0.0 | NaN | 0.0 | Consumer credit | -131 | NaN |
| 1 | 215354 | 5714463 | Active | currency 1 | -208 | 0 | 1075.0 | NaN | NaN | 0 | 225000.0 | 171342.0 | NaN | 0.0 | Credit card | -20 | NaN |
| 2 | 215354 | 5714464 | Active | currency 1 | -203 | 0 | 528.0 | NaN | NaN | 0 | 464323.5 | NaN | NaN | 0.0 | Consumer credit | -16 | NaN |
| 3 | 215354 | 5714465 | Active | currency 1 | -203 | 0 | NaN | NaN | NaN | 0 | 90000.0 | NaN | NaN | 0.0 | Credit card | -16 | NaN |
| 4 | 215354 | 5714466 | Active | currency 1 | -629 | 0 | 1197.0 | NaN | 77674.5 | 0 | 2700000.0 | NaN | NaN | 0.0 | Consumer credit | -21 | NaN |
bureau_balance: shape is (27299925, 3) <class 'pandas.core.frame.DataFrame'> RangeIndex: 27299925 entries, 0 to 27299924 Data columns (total 3 columns): # Column Dtype --- ------ ----- 0 SK_ID_BUREAU int64 1 MONTHS_BALANCE int64 2 STATUS object dtypes: int64(2), object(1) memory usage: 624.8+ MB None
| SK_ID_BUREAU | MONTHS_BALANCE | STATUS | |
|---|---|---|---|
| 0 | 5715448 | 0 | C |
| 1 | 5715448 | -1 | C |
| 2 | 5715448 | -2 | C |
| 3 | 5715448 | -3 | C |
| 4 | 5715448 | -4 | C |
credit_card_balance: shape is (3840312, 23) <class 'pandas.core.frame.DataFrame'> RangeIndex: 3840312 entries, 0 to 3840311 Data columns (total 23 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 MONTHS_BALANCE int64 3 AMT_BALANCE float64 4 AMT_CREDIT_LIMIT_ACTUAL int64 5 AMT_DRAWINGS_ATM_CURRENT float64 6 AMT_DRAWINGS_CURRENT float64 7 AMT_DRAWINGS_OTHER_CURRENT float64 8 AMT_DRAWINGS_POS_CURRENT float64 9 AMT_INST_MIN_REGULARITY float64 10 AMT_PAYMENT_CURRENT float64 11 AMT_PAYMENT_TOTAL_CURRENT float64 12 AMT_RECEIVABLE_PRINCIPAL float64 13 AMT_RECIVABLE float64 14 AMT_TOTAL_RECEIVABLE float64 15 CNT_DRAWINGS_ATM_CURRENT float64 16 CNT_DRAWINGS_CURRENT int64 17 CNT_DRAWINGS_OTHER_CURRENT float64 18 CNT_DRAWINGS_POS_CURRENT float64 19 CNT_INSTALMENT_MATURE_CUM float64 20 NAME_CONTRACT_STATUS object 21 SK_DPD int64 22 SK_DPD_DEF int64 dtypes: float64(15), int64(7), object(1) memory usage: 673.9+ MB None
| SK_ID_PREV | SK_ID_CURR | MONTHS_BALANCE | AMT_BALANCE | AMT_CREDIT_LIMIT_ACTUAL | AMT_DRAWINGS_ATM_CURRENT | AMT_DRAWINGS_CURRENT | AMT_DRAWINGS_OTHER_CURRENT | AMT_DRAWINGS_POS_CURRENT | AMT_INST_MIN_REGULARITY | ... | AMT_RECIVABLE | AMT_TOTAL_RECEIVABLE | CNT_DRAWINGS_ATM_CURRENT | CNT_DRAWINGS_CURRENT | CNT_DRAWINGS_OTHER_CURRENT | CNT_DRAWINGS_POS_CURRENT | CNT_INSTALMENT_MATURE_CUM | NAME_CONTRACT_STATUS | SK_DPD | SK_DPD_DEF | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2562384 | 378907 | -6 | 56.970 | 135000 | 0.0 | 877.5 | 0.0 | 877.5 | 1700.325 | ... | 0.000 | 0.000 | 0.0 | 1 | 0.0 | 1.0 | 35.0 | Active | 0 | 0 |
| 1 | 2582071 | 363914 | -1 | 63975.555 | 45000 | 2250.0 | 2250.0 | 0.0 | 0.0 | 2250.000 | ... | 64875.555 | 64875.555 | 1.0 | 1 | 0.0 | 0.0 | 69.0 | Active | 0 | 0 |
| 2 | 1740877 | 371185 | -7 | 31815.225 | 450000 | 0.0 | 0.0 | 0.0 | 0.0 | 2250.000 | ... | 31460.085 | 31460.085 | 0.0 | 0 | 0.0 | 0.0 | 30.0 | Active | 0 | 0 |
| 3 | 1389973 | 337855 | -4 | 236572.110 | 225000 | 2250.0 | 2250.0 | 0.0 | 0.0 | 11795.760 | ... | 233048.970 | 233048.970 | 1.0 | 1 | 0.0 | 0.0 | 10.0 | Active | 0 | 0 |
| 4 | 1891521 | 126868 | -1 | 453919.455 | 450000 | 0.0 | 11547.0 | 0.0 | 11547.0 | 22924.890 | ... | 453919.455 | 453919.455 | 0.0 | 1 | 0.0 | 1.0 | 101.0 | Active | 0 | 0 |
5 rows × 23 columns
installments_payments: shape is (13605401, 8) <class 'pandas.core.frame.DataFrame'> RangeIndex: 13605401 entries, 0 to 13605400 Data columns (total 8 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 NUM_INSTALMENT_VERSION float64 3 NUM_INSTALMENT_NUMBER int64 4 DAYS_INSTALMENT float64 5 DAYS_ENTRY_PAYMENT float64 6 AMT_INSTALMENT float64 7 AMT_PAYMENT float64 dtypes: float64(5), int64(3) memory usage: 830.4 MB None
| SK_ID_PREV | SK_ID_CURR | NUM_INSTALMENT_VERSION | NUM_INSTALMENT_NUMBER | DAYS_INSTALMENT | DAYS_ENTRY_PAYMENT | AMT_INSTALMENT | AMT_PAYMENT | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1054186 | 161674 | 1.0 | 6 | -1180.0 | -1187.0 | 6948.360 | 6948.360 |
| 1 | 1330831 | 151639 | 0.0 | 34 | -2156.0 | -2156.0 | 1716.525 | 1716.525 |
| 2 | 2085231 | 193053 | 2.0 | 1 | -63.0 | -63.0 | 25425.000 | 25425.000 |
| 3 | 2452527 | 199697 | 1.0 | 3 | -2418.0 | -2426.0 | 24350.130 | 24350.130 |
| 4 | 2714724 | 167756 | 1.0 | 2 | -1383.0 | -1366.0 | 2165.040 | 2160.585 |
previous_application: shape is (1670214, 37) <class 'pandas.core.frame.DataFrame'> RangeIndex: 1670214 entries, 0 to 1670213 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 SK_ID_PREV 1670214 non-null int64 1 SK_ID_CURR 1670214 non-null int64 2 NAME_CONTRACT_TYPE 1670214 non-null object 3 AMT_ANNUITY 1297979 non-null float64 4 AMT_APPLICATION 1670214 non-null float64 5 AMT_CREDIT 1670213 non-null float64 6 AMT_DOWN_PAYMENT 774370 non-null float64 7 AMT_GOODS_PRICE 1284699 non-null float64 8 WEEKDAY_APPR_PROCESS_START 1670214 non-null object 9 HOUR_APPR_PROCESS_START 1670214 non-null int64 10 FLAG_LAST_APPL_PER_CONTRACT 1670214 non-null object 11 NFLAG_LAST_APPL_IN_DAY 1670214 non-null int64 12 RATE_DOWN_PAYMENT 774370 non-null float64 13 RATE_INTEREST_PRIMARY 5951 non-null float64 14 RATE_INTEREST_PRIVILEGED 5951 non-null float64 15 NAME_CASH_LOAN_PURPOSE 1670214 non-null object 16 NAME_CONTRACT_STATUS 1670214 non-null object 17 DAYS_DECISION 1670214 non-null int64 18 NAME_PAYMENT_TYPE 1670214 non-null object 19 CODE_REJECT_REASON 1670214 non-null object 20 NAME_TYPE_SUITE 849809 non-null object 21 NAME_CLIENT_TYPE 1670214 non-null object 22 NAME_GOODS_CATEGORY 1670214 non-null object 23 NAME_PORTFOLIO 1670214 non-null object 24 NAME_PRODUCT_TYPE 1670214 non-null object 25 CHANNEL_TYPE 1670214 non-null object 26 SELLERPLACE_AREA 1670214 non-null int64 27 NAME_SELLER_INDUSTRY 1670214 non-null object 28 CNT_PAYMENT 1297984 non-null float64 29 NAME_YIELD_GROUP 1670214 non-null object 30 PRODUCT_COMBINATION 1669868 non-null object 31 DAYS_FIRST_DRAWING 997149 non-null float64 32 DAYS_FIRST_DUE 997149 non-null float64 33 DAYS_LAST_DUE_1ST_VERSION 997149 non-null float64 34 DAYS_LAST_DUE 997149 non-null float64 35 DAYS_TERMINATION 997149 non-null float64 36 NFLAG_INSURED_ON_APPROVAL 997149 non-null float64 dtypes: float64(15), int64(6), object(16) memory usage: 471.5+ MB None
| SK_ID_PREV | SK_ID_CURR | NAME_CONTRACT_TYPE | AMT_ANNUITY | AMT_APPLICATION | AMT_CREDIT | AMT_DOWN_PAYMENT | AMT_GOODS_PRICE | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | ... | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2030495 | 271877 | Consumer loans | 1730.430 | 17145.0 | 17145.0 | 0.0 | 17145.0 | SATURDAY | 15 | ... | Connectivity | 12.0 | middle | POS mobile with interest | 365243.0 | -42.0 | 300.0 | -42.0 | -37.0 | 0.0 |
| 1 | 2802425 | 108129 | Cash loans | 25188.615 | 607500.0 | 679671.0 | NaN | 607500.0 | THURSDAY | 11 | ... | XNA | 36.0 | low_action | Cash X-Sell: low | 365243.0 | -134.0 | 916.0 | 365243.0 | 365243.0 | 1.0 |
| 2 | 2523466 | 122040 | Cash loans | 15060.735 | 112500.0 | 136444.5 | NaN | 112500.0 | TUESDAY | 11 | ... | XNA | 12.0 | high | Cash X-Sell: high | 365243.0 | -271.0 | 59.0 | 365243.0 | 365243.0 | 1.0 |
| 3 | 2819243 | 176158 | Cash loans | 47041.335 | 450000.0 | 470790.0 | NaN | 450000.0 | MONDAY | 7 | ... | XNA | 12.0 | middle | Cash X-Sell: middle | 365243.0 | -482.0 | -152.0 | -182.0 | -177.0 | 1.0 |
| 4 | 1784265 | 202054 | Cash loans | 31924.395 | 337500.0 | 404055.0 | NaN | 337500.0 | THURSDAY | 9 | ... | XNA | 24.0 | high | Cash Street: high | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 37 columns
POS_CASH_balance: shape is (10001358, 8) <class 'pandas.core.frame.DataFrame'> RangeIndex: 10001358 entries, 0 to 10001357 Data columns (total 8 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 MONTHS_BALANCE int64 3 CNT_INSTALMENT float64 4 CNT_INSTALMENT_FUTURE float64 5 NAME_CONTRACT_STATUS object 6 SK_DPD int64 7 SK_DPD_DEF int64 dtypes: float64(2), int64(5), object(1) memory usage: 610.4+ MB None
| SK_ID_PREV | SK_ID_CURR | MONTHS_BALANCE | CNT_INSTALMENT | CNT_INSTALMENT_FUTURE | NAME_CONTRACT_STATUS | SK_DPD | SK_DPD_DEF | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1803195 | 182943 | -31 | 48.0 | 45.0 | Active | 0 | 0 |
| 1 | 1715348 | 367990 | -33 | 36.0 | 35.0 | Active | 0 | 0 |
| 2 | 1784872 | 397406 | -32 | 12.0 | 9.0 | Active | 0 | 0 |
| 3 | 1903291 | 269225 | -35 | 48.0 | 42.0 | Active | 0 | 0 |
| 4 | 2341044 | 334279 | -35 | 36.0 | 35.0 | Active | 0 | 0 |
CPU times: total: 18.2 s Wall time: 22.4 s
for ds_name in datasets.keys():
print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')
freshdata = datasets
dataset application_train : [ 307,511, 122] dataset application_test : [ 48,744, 121] dataset bureau : [ 1,716,428, 17] dataset bureau_balance : [ 27,299,925, 3] dataset credit_card_balance : [ 3,840,312, 23] dataset installments_payments : [ 13,605,401, 8] dataset previous_application : [ 1,670,214, 37] dataset POS_CASH_balance : [ 10,001,358, 8]
datasets["application_train"].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR dtypes: float64(65), int64(41), object(16) memory usage: 286.2+ MB
datasets["application_train"].describe() #numerical only features
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.000000 | 307511.000000 | 307511.000000 | 3.075110e+05 | 3.075110e+05 | 307499.000000 | 3.072330e+05 | 307511.000000 | 307511.000000 | 307511.000000 | ... | 307511.000000 | 307511.000000 | 307511.000000 | 307511.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 |
| mean | 278180.518577 | 0.080729 | 0.417052 | 1.687979e+05 | 5.990260e+05 | 27108.573909 | 5.383962e+05 | 0.020868 | -16036.995067 | 63815.045904 | ... | 0.008130 | 0.000595 | 0.000507 | 0.000335 | 0.006402 | 0.007000 | 0.034362 | 0.267395 | 0.265474 | 1.899974 |
| std | 102790.175348 | 0.272419 | 0.722121 | 2.371231e+05 | 4.024908e+05 | 14493.737315 | 3.694465e+05 | 0.013831 | 4363.988632 | 141275.766519 | ... | 0.089798 | 0.024387 | 0.022518 | 0.018299 | 0.083849 | 0.110757 | 0.204685 | 0.916002 | 0.794056 | 1.869295 |
| min | 100002.000000 | 0.000000 | 0.000000 | 2.565000e+04 | 4.500000e+04 | 1615.500000 | 4.050000e+04 | 0.000290 | -25229.000000 | -17912.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 189145.500000 | 0.000000 | 0.000000 | 1.125000e+05 | 2.700000e+05 | 16524.000000 | 2.385000e+05 | 0.010006 | -19682.000000 | -2760.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 278202.000000 | 0.000000 | 0.000000 | 1.471500e+05 | 5.135310e+05 | 24903.000000 | 4.500000e+05 | 0.018850 | -15750.000000 | -1213.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 367142.500000 | 0.000000 | 1.000000 | 2.025000e+05 | 8.086500e+05 | 34596.000000 | 6.795000e+05 | 0.028663 | -12413.000000 | -289.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 |
| max | 456255.000000 | 1.000000 | 19.000000 | 1.170000e+08 | 4.050000e+06 | 258025.500000 | 4.050000e+06 | 0.072508 | -7489.000000 | 365243.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 9.000000 | 8.000000 | 27.000000 | 261.000000 | 25.000000 |
8 rows × 106 columns
datasets["application_test"].describe() #numerical only features
| SK_ID_CURR | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 48744.000000 | 48744.000000 | 4.874400e+04 | 4.874400e+04 | 48720.000000 | 4.874400e+04 | 48744.000000 | 48744.000000 | 48744.000000 | 48744.000000 | ... | 48744.000000 | 48744.0 | 48744.0 | 48744.0 | 42695.000000 | 42695.000000 | 42695.000000 | 42695.000000 | 42695.000000 | 42695.000000 |
| mean | 277796.676350 | 0.397054 | 1.784318e+05 | 5.167404e+05 | 29426.240209 | 4.626188e+05 | 0.021226 | -16068.084605 | 67485.366322 | -4967.652716 | ... | 0.001559 | 0.0 | 0.0 | 0.0 | 0.002108 | 0.001803 | 0.002787 | 0.009299 | 0.546902 | 1.983769 |
| std | 103169.547296 | 0.709047 | 1.015226e+05 | 3.653970e+05 | 16016.368315 | 3.367102e+05 | 0.014428 | 4325.900393 | 144348.507136 | 3552.612035 | ... | 0.039456 | 0.0 | 0.0 | 0.0 | 0.046373 | 0.046132 | 0.054037 | 0.110924 | 0.693305 | 1.838873 |
| min | 100001.000000 | 0.000000 | 2.694150e+04 | 4.500000e+04 | 2295.000000 | 4.500000e+04 | 0.000253 | -25195.000000 | -17463.000000 | -23722.000000 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 188557.750000 | 0.000000 | 1.125000e+05 | 2.606400e+05 | 17973.000000 | 2.250000e+05 | 0.010006 | -19637.000000 | -2910.000000 | -7459.250000 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 277549.000000 | 0.000000 | 1.575000e+05 | 4.500000e+05 | 26199.000000 | 3.960000e+05 | 0.018850 | -15785.000000 | -1293.000000 | -4490.000000 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 |
| 75% | 367555.500000 | 1.000000 | 2.250000e+05 | 6.750000e+05 | 37390.500000 | 6.300000e+05 | 0.028663 | -12496.000000 | -296.000000 | -1901.000000 | ... | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 |
| max | 456250.000000 | 20.000000 | 4.410000e+06 | 2.245500e+06 | 180576.000000 | 2.245500e+06 | 0.072508 | -7338.000000 | 365243.000000 | 0.000000 | ... | 1.000000 | 0.0 | 0.0 | 0.0 | 2.000000 | 2.000000 | 2.000000 | 6.000000 | 7.000000 | 17.000000 |
8 rows × 105 columns
datasets["application_train"].describe(include='all') #look at all categorical and numerical
| SK_ID_CURR | TARGET | NAME_CONTRACT_TYPE | CODE_GENDER | FLAG_OWN_CAR | FLAG_OWN_REALTY | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | ... | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.000000 | 307511.000000 | 307511 | 307511 | 307511 | 307511 | 307511.000000 | 3.075110e+05 | 3.075110e+05 | 307499.000000 | ... | 307511.000000 | 307511.000000 | 307511.000000 | 307511.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 | 265992.000000 |
| unique | NaN | NaN | 2 | 3 | 2 | 2 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| top | NaN | NaN | Cash loans | F | N | Y | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| freq | NaN | NaN | 278232 | 202448 | 202924 | 213312 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| mean | 278180.518577 | 0.080729 | NaN | NaN | NaN | NaN | 0.417052 | 1.687979e+05 | 5.990260e+05 | 27108.573909 | ... | 0.008130 | 0.000595 | 0.000507 | 0.000335 | 0.006402 | 0.007000 | 0.034362 | 0.267395 | 0.265474 | 1.899974 |
| std | 102790.175348 | 0.272419 | NaN | NaN | NaN | NaN | 0.722121 | 2.371231e+05 | 4.024908e+05 | 14493.737315 | ... | 0.089798 | 0.024387 | 0.022518 | 0.018299 | 0.083849 | 0.110757 | 0.204685 | 0.916002 | 0.794056 | 1.869295 |
| min | 100002.000000 | 0.000000 | NaN | NaN | NaN | NaN | 0.000000 | 2.565000e+04 | 4.500000e+04 | 1615.500000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 189145.500000 | 0.000000 | NaN | NaN | NaN | NaN | 0.000000 | 1.125000e+05 | 2.700000e+05 | 16524.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 278202.000000 | 0.000000 | NaN | NaN | NaN | NaN | 0.000000 | 1.471500e+05 | 5.135310e+05 | 24903.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| 75% | 367142.500000 | 0.000000 | NaN | NaN | NaN | NaN | 1.000000 | 2.025000e+05 | 8.086500e+05 | 34596.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 |
| max | 456255.000000 | 1.000000 | NaN | NaN | NaN | NaN | 19.000000 | 1.170000e+08 | 4.050000e+06 | 258025.500000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 4.000000 | 9.000000 | 8.000000 | 27.000000 | 261.000000 | 25.000000 |
11 rows × 122 columns
percent = (datasets["application_train"].isnull().sum()/datasets["application_train"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_train"].isna().sum().sort_values(ascending = False)
missing_application_train_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)
| Percent | Train Missing Count | |
|---|---|---|
| COMMONAREA_MEDI | 69.87 | 214865 |
| COMMONAREA_AVG | 69.87 | 214865 |
| COMMONAREA_MODE | 69.87 | 214865 |
| NONLIVINGAPARTMENTS_MODE | 69.43 | 213514 |
| NONLIVINGAPARTMENTS_AVG | 69.43 | 213514 |
| NONLIVINGAPARTMENTS_MEDI | 69.43 | 213514 |
| FONDKAPREMONT_MODE | 68.39 | 210295 |
| LIVINGAPARTMENTS_MODE | 68.35 | 210199 |
| LIVINGAPARTMENTS_AVG | 68.35 | 210199 |
| LIVINGAPARTMENTS_MEDI | 68.35 | 210199 |
| FLOORSMIN_AVG | 67.85 | 208642 |
| FLOORSMIN_MODE | 67.85 | 208642 |
| FLOORSMIN_MEDI | 67.85 | 208642 |
| YEARS_BUILD_MEDI | 66.50 | 204488 |
| YEARS_BUILD_MODE | 66.50 | 204488 |
| YEARS_BUILD_AVG | 66.50 | 204488 |
| OWN_CAR_AGE | 65.99 | 202929 |
| LANDAREA_MEDI | 59.38 | 182590 |
| LANDAREA_MODE | 59.38 | 182590 |
| LANDAREA_AVG | 59.38 | 182590 |
percent = (datasets["application_test"].isnull().sum()/datasets["application_test"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_test"].isna().sum().sort_values(ascending = False)
missing_application_train_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_train_data.head(20)
| Percent | Test Missing Count | |
|---|---|---|
| COMMONAREA_AVG | 68.72 | 33495 |
| COMMONAREA_MODE | 68.72 | 33495 |
| COMMONAREA_MEDI | 68.72 | 33495 |
| NONLIVINGAPARTMENTS_AVG | 68.41 | 33347 |
| NONLIVINGAPARTMENTS_MODE | 68.41 | 33347 |
| NONLIVINGAPARTMENTS_MEDI | 68.41 | 33347 |
| FONDKAPREMONT_MODE | 67.28 | 32797 |
| LIVINGAPARTMENTS_AVG | 67.25 | 32780 |
| LIVINGAPARTMENTS_MODE | 67.25 | 32780 |
| LIVINGAPARTMENTS_MEDI | 67.25 | 32780 |
| FLOORSMIN_MEDI | 66.61 | 32466 |
| FLOORSMIN_AVG | 66.61 | 32466 |
| FLOORSMIN_MODE | 66.61 | 32466 |
| OWN_CAR_AGE | 66.29 | 32312 |
| YEARS_BUILD_AVG | 65.28 | 31818 |
| YEARS_BUILD_MEDI | 65.28 | 31818 |
| YEARS_BUILD_MODE | 65.28 | 31818 |
| LANDAREA_MEDI | 57.96 | 28254 |
| LANDAREA_AVG | 57.96 | 28254 |
| LANDAREA_MODE | 57.96 | 28254 |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
# Plotting the missing data heatmap
plt.figure(figsize=(12, 8))
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.title(f'Missing Data Heatmap for {df_name}', fontsize=16)
plt.show()
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(missing_data["Percent"], missing_data.index, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["application_train"], "application_train")
display_feature_info(datasets["application_train"], "application_train")
--------------------------------------------------------------------------------
application_train
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 122 columns):
# Column Dtype
--- ------ -----
0 SK_ID_CURR int64
1 TARGET int64
2 NAME_CONTRACT_TYPE object
3 CODE_GENDER object
4 FLAG_OWN_CAR object
5 FLAG_OWN_REALTY object
6 CNT_CHILDREN int64
7 AMT_INCOME_TOTAL float64
8 AMT_CREDIT float64
9 AMT_ANNUITY float64
10 AMT_GOODS_PRICE float64
11 NAME_TYPE_SUITE object
12 NAME_INCOME_TYPE object
13 NAME_EDUCATION_TYPE object
14 NAME_FAMILY_STATUS object
15 NAME_HOUSING_TYPE object
16 REGION_POPULATION_RELATIVE float64
17 DAYS_BIRTH int64
18 DAYS_EMPLOYED int64
19 DAYS_REGISTRATION float64
20 DAYS_ID_PUBLISH int64
21 OWN_CAR_AGE float64
22 FLAG_MOBIL int64
23 FLAG_EMP_PHONE int64
24 FLAG_WORK_PHONE int64
25 FLAG_CONT_MOBILE int64
26 FLAG_PHONE int64
27 FLAG_EMAIL int64
28 OCCUPATION_TYPE object
29 CNT_FAM_MEMBERS float64
30 REGION_RATING_CLIENT int64
31 REGION_RATING_CLIENT_W_CITY int64
32 WEEKDAY_APPR_PROCESS_START object
33 HOUR_APPR_PROCESS_START int64
34 REG_REGION_NOT_LIVE_REGION int64
35 REG_REGION_NOT_WORK_REGION int64
36 LIVE_REGION_NOT_WORK_REGION int64
37 REG_CITY_NOT_LIVE_CITY int64
38 REG_CITY_NOT_WORK_CITY int64
39 LIVE_CITY_NOT_WORK_CITY int64
40 ORGANIZATION_TYPE object
41 EXT_SOURCE_1 float64
42 EXT_SOURCE_2 float64
43 EXT_SOURCE_3 float64
44 APARTMENTS_AVG float64
45 BASEMENTAREA_AVG float64
46 YEARS_BEGINEXPLUATATION_AVG float64
47 YEARS_BUILD_AVG float64
48 COMMONAREA_AVG float64
49 ELEVATORS_AVG float64
50 ENTRANCES_AVG float64
51 FLOORSMAX_AVG float64
52 FLOORSMIN_AVG float64
53 LANDAREA_AVG float64
54 LIVINGAPARTMENTS_AVG float64
55 LIVINGAREA_AVG float64
56 NONLIVINGAPARTMENTS_AVG float64
57 NONLIVINGAREA_AVG float64
58 APARTMENTS_MODE float64
59 BASEMENTAREA_MODE float64
60 YEARS_BEGINEXPLUATATION_MODE float64
61 YEARS_BUILD_MODE float64
62 COMMONAREA_MODE float64
63 ELEVATORS_MODE float64
64 ENTRANCES_MODE float64
65 FLOORSMAX_MODE float64
66 FLOORSMIN_MODE float64
67 LANDAREA_MODE float64
68 LIVINGAPARTMENTS_MODE float64
69 LIVINGAREA_MODE float64
70 NONLIVINGAPARTMENTS_MODE float64
71 NONLIVINGAREA_MODE float64
72 APARTMENTS_MEDI float64
73 BASEMENTAREA_MEDI float64
74 YEARS_BEGINEXPLUATATION_MEDI float64
75 YEARS_BUILD_MEDI float64
76 COMMONAREA_MEDI float64
77 ELEVATORS_MEDI float64
78 ENTRANCES_MEDI float64
79 FLOORSMAX_MEDI float64
80 FLOORSMIN_MEDI float64
81 LANDAREA_MEDI float64
82 LIVINGAPARTMENTS_MEDI float64
83 LIVINGAREA_MEDI float64
84 NONLIVINGAPARTMENTS_MEDI float64
85 NONLIVINGAREA_MEDI float64
86 FONDKAPREMONT_MODE object
87 HOUSETYPE_MODE object
88 TOTALAREA_MODE float64
89 WALLSMATERIAL_MODE object
90 EMERGENCYSTATE_MODE object
91 OBS_30_CNT_SOCIAL_CIRCLE float64
92 DEF_30_CNT_SOCIAL_CIRCLE float64
93 OBS_60_CNT_SOCIAL_CIRCLE float64
94 DEF_60_CNT_SOCIAL_CIRCLE float64
95 DAYS_LAST_PHONE_CHANGE float64
96 FLAG_DOCUMENT_2 int64
97 FLAG_DOCUMENT_3 int64
98 FLAG_DOCUMENT_4 int64
99 FLAG_DOCUMENT_5 int64
100 FLAG_DOCUMENT_6 int64
101 FLAG_DOCUMENT_7 int64
102 FLAG_DOCUMENT_8 int64
103 FLAG_DOCUMENT_9 int64
104 FLAG_DOCUMENT_10 int64
105 FLAG_DOCUMENT_11 int64
106 FLAG_DOCUMENT_12 int64
107 FLAG_DOCUMENT_13 int64
108 FLAG_DOCUMENT_14 int64
109 FLAG_DOCUMENT_15 int64
110 FLAG_DOCUMENT_16 int64
111 FLAG_DOCUMENT_17 int64
112 FLAG_DOCUMENT_18 int64
113 FLAG_DOCUMENT_19 int64
114 FLAG_DOCUMENT_20 int64
115 FLAG_DOCUMENT_21 int64
116 AMT_REQ_CREDIT_BUREAU_HOUR float64
117 AMT_REQ_CREDIT_BUREAU_DAY float64
118 AMT_REQ_CREDIT_BUREAU_WEEK float64
119 AMT_REQ_CREDIT_BUREAU_MON float64
120 AMT_REQ_CREDIT_BUREAU_QRT float64
121 AMT_REQ_CREDIT_BUREAU_YEAR float64
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None
---------------------------------------------------------------------------
Shape of the df application_train is (307511, 122)
---------------------------------------------------------------------------
Statistical summary of application_train is :
---------------------------------------------------------------------------
Description of the df application_train:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for application_train:
---------------------------------------------------------------------------
Data type value counts:
float64 65
int64 41
object 16
Name: count, dtype: int64
Return the number of unique elements in the object.
NAME_CONTRACT_TYPE 2
CODE_GENDER 3
FLAG_OWN_CAR 2
FLAG_OWN_REALTY 2
NAME_TYPE_SUITE 7
NAME_INCOME_TYPE 8
NAME_EDUCATION_TYPE 5
NAME_FAMILY_STATUS 6
NAME_HOUSING_TYPE 6
OCCUPATION_TYPE 18
WEEKDAY_APPR_PROCESS_START 7
ORGANIZATION_TYPE 58
FONDKAPREMONT_MODE 4
HOUSETYPE_MODE 3
WALLSMATERIAL_MODE 7
EMERGENCYSTATE_MODE 2
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of application_train.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_CURR', 'TARGET', 'CNT_CHILDREN', 'DAYS_BIRTH', 'DAYS_EMPLOYED',
'DAYS_ID_PUBLISH', 'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',
'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL', 'REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY', 'HOUR_APPR_PROCESS_START',
'REG_REGION_NOT_LIVE_REGION', 'REG_REGION_NOT_WORK_REGION',
'LIVE_REGION_NOT_WORK_REGION', 'REG_CITY_NOT_LIVE_CITY',
'REG_CITY_NOT_WORK_CITY', 'LIVE_CITY_NOT_WORK_CITY', 'FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3', 'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5',
'FLAG_DOCUMENT_6', 'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8',
'FLAG_DOCUMENT_9', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11',
'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14',
'FLAG_DOCUMENT_15', 'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
'FLAG_DOCUMENT_21'],
dtype='object')}
------------------------------
{'float64': Index(['AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE',
'REGION_POPULATION_RELATIVE', 'DAYS_REGISTRATION', 'OWN_CAR_AGE',
'CNT_FAM_MEMBERS', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3',
'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG',
'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG',
'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG',
'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG',
'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE',
'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE',
'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE',
'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE',
'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI',
'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI',
'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI',
'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI',
'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI',
'TOTALAREA_MODE', 'OBS_30_CNT_SOCIAL_CIRCLE',
'DEF_30_CNT_SOCIAL_CIRCLE', 'OBS_60_CNT_SOCIAL_CIRCLE',
'DEF_60_CNT_SOCIAL_CIRCLE', 'DAYS_LAST_PHONE_CHANGE',
'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR'],
dtype='object')}
------------------------------
{'object': Index(['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY',
'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE',
'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE',
'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'FONDKAPREMONT_MODE',
'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE'],
dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| COMMONAREA_MEDI | 69.87 | 214865 |
| COMMONAREA_AVG | 69.87 | 214865 |
| COMMONAREA_MODE | 69.87 | 214865 |
| NONLIVINGAPARTMENTS_MODE | 69.43 | 213514 |
| NONLIVINGAPARTMENTS_AVG | 69.43 | 213514 |
| NONLIVINGAPARTMENTS_MEDI | 69.43 | 213514 |
| FONDKAPREMONT_MODE | 68.39 | 210295 |
| LIVINGAPARTMENTS_MODE | 68.35 | 210199 |
| LIVINGAPARTMENTS_AVG | 68.35 | 210199 |
| LIVINGAPARTMENTS_MEDI | 68.35 | 210199 |
| FLOORSMIN_AVG | 67.85 | 208642 |
| FLOORSMIN_MODE | 67.85 | 208642 |
| FLOORSMIN_MEDI | 67.85 | 208642 |
| YEARS_BUILD_MEDI | 66.50 | 204488 |
| YEARS_BUILD_MODE | 66.50 | 204488 |
| YEARS_BUILD_AVG | 66.50 | 204488 |
| OWN_CAR_AGE | 65.99 | 202929 |
| LANDAREA_MEDI | 59.38 | 182590 |
| LANDAREA_MODE | 59.38 | 182590 |
| LANDAREA_AVG | 59.38 | 182590 |
| BASEMENTAREA_MEDI | 58.52 | 179943 |
| BASEMENTAREA_AVG | 58.52 | 179943 |
| BASEMENTAREA_MODE | 58.52 | 179943 |
| EXT_SOURCE_1 | 56.38 | 173378 |
| NONLIVINGAREA_MODE | 55.18 | 169682 |
| NONLIVINGAREA_AVG | 55.18 | 169682 |
| NONLIVINGAREA_MEDI | 55.18 | 169682 |
| ELEVATORS_MEDI | 53.30 | 163891 |
| ELEVATORS_AVG | 53.30 | 163891 |
| ELEVATORS_MODE | 53.30 | 163891 |
| WALLSMATERIAL_MODE | 50.84 | 156341 |
| APARTMENTS_MEDI | 50.75 | 156061 |
| APARTMENTS_AVG | 50.75 | 156061 |
| APARTMENTS_MODE | 50.75 | 156061 |
| ENTRANCES_MEDI | 50.35 | 154828 |
| ENTRANCES_AVG | 50.35 | 154828 |
| ENTRANCES_MODE | 50.35 | 154828 |
| LIVINGAREA_AVG | 50.19 | 154350 |
| LIVINGAREA_MODE | 50.19 | 154350 |
| LIVINGAREA_MEDI | 50.19 | 154350 |
| HOUSETYPE_MODE | 50.18 | 154297 |
| FLOORSMAX_MODE | 49.76 | 153020 |
| FLOORSMAX_MEDI | 49.76 | 153020 |
| FLOORSMAX_AVG | 49.76 | 153020 |
| YEARS_BEGINEXPLUATATION_MODE | 48.78 | 150007 |
| YEARS_BEGINEXPLUATATION_MEDI | 48.78 | 150007 |
| YEARS_BEGINEXPLUATATION_AVG | 48.78 | 150007 |
| TOTALAREA_MODE | 48.27 | 148431 |
| EMERGENCYSTATE_MODE | 47.40 | 145755 |
| OCCUPATION_TYPE | 31.35 | 96391 |
| EXT_SOURCE_3 | 19.83 | 60965 |
| AMT_REQ_CREDIT_BUREAU_HOUR | 13.50 | 41519 |
| AMT_REQ_CREDIT_BUREAU_DAY | 13.50 | 41519 |
| AMT_REQ_CREDIT_BUREAU_WEEK | 13.50 | 41519 |
| AMT_REQ_CREDIT_BUREAU_MON | 13.50 | 41519 |
| AMT_REQ_CREDIT_BUREAU_QRT | 13.50 | 41519 |
| AMT_REQ_CREDIT_BUREAU_YEAR | 13.50 | 41519 |
| NAME_TYPE_SUITE | 0.42 | 1292 |
| OBS_30_CNT_SOCIAL_CIRCLE | 0.33 | 1021 |
| DEF_30_CNT_SOCIAL_CIRCLE | 0.33 | 1021 |
| OBS_60_CNT_SOCIAL_CIRCLE | 0.33 | 1021 |
| DEF_60_CNT_SOCIAL_CIRCLE | 0.33 | 1021 |
| EXT_SOURCE_2 | 0.21 | 660 |
| AMT_GOODS_PRICE | 0.09 | 278 |
---------------------------------------------------------------------------
Anomalies are apparent in the dataset based on descriptive statistics for features like Days Birth, Days Employed, Days Registration, and Days ID Publish, where negative values are present, which is unexpected.
The maximum value for Own Car Age is 91.
Certain features related to living space and realty appear to be redundant, and considering their removal during the feature reduction process would be beneficial to mitigate potential issues with multicollinearity.
pip install seaborn --upgrade
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Installing collected packages: seaborn
Attempting uninstall: seaborn
Found existing installation: seaborn 0.11.2
Uninstalling seaborn-0.11.2:
Successfully uninstalled seaborn-0.11.2
Successfully installed seaborn-0.13.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.3.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
df_train = datasets["application_train"]
import matplotlib.pyplot as plt
import seaborn as sns
# Set up a function to handle NaN and infinite values for histograms
def plot_histogram(feature, xlabel, title, color, xlim=None):
plt.figure(figsize=(10, 6))
sns.histplot(df_train[feature].replace([np.inf, -np.inf], np.nan).dropna(), kde=False, bins=30, color=color)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel('Frequency')
if xlim:
plt.xlim(xlim)
plt.show()
# OWN_CAR_AGE: Histogram for negative values
plot_histogram('OWN_CAR_AGE', 'Own Car Age', 'Distribution of Own Car Age (Negative Values)', 'skyblue', xlim=(df_train['OWN_CAR_AGE'].min(), 0))
# DAYS_BIRTH: Age Distribution
plot_histogram('DAYS_BIRTH', 'Age (years)', 'Distribution of Age', 'salmon')
# DAYS_EMPLOYED: Employment Duration Distribution
plot_histogram('DAYS_EMPLOYED', 'Employment Duration (years)', 'Distribution of Employment Duration', 'lightgreen', xlim=(df_train['DAYS_EMPLOYED'].min(), 0))
# DAYS_REGISTRATION: Days Since Registration Distribution
plot_histogram('DAYS_REGISTRATION', 'Days Since Registration (years)', 'Distribution of Days Since Registration', 'orange', xlim=(df_train['DAYS_REGISTRATION'].min(), 0))
The training dataset, referred to as "Application Train," contains extensive information about submitted loan requests.
However, the presence of missing values is a notable concern within this dataset. Particularly, Occupation Type and Organization Type are categorical variables with 58 and 18 categories, respectively.
These categorical features hold the potential for valuable insights in the process of feature engineering.
plt.figure(figsize=(10,5))
sns.set_theme()
sns.countplot(x = 'TARGET',data = df_train)
plt.xlabel("Target",fontweight='bold',size=13)
plt.ylabel("Count",fontweight='bold',size=13)
plt.show()
correlations = datasets["application_train"].corr()['TARGET'].sort_values()
print('Most Positive Correlations:\n', correlations.tail(10))
print('\nMost Negative Correlations:\n', correlations.head(10))
Most Positive Correlations: FLAG_DOCUMENT_3 0.044346 REG_CITY_NOT_LIVE_CITY 0.044395 FLAG_EMP_PHONE 0.045982 REG_CITY_NOT_WORK_CITY 0.050994 DAYS_ID_PUBLISH 0.051457 DAYS_LAST_PHONE_CHANGE 0.055218 REGION_RATING_CLIENT 0.058899 REGION_RATING_CLIENT_W_CITY 0.060893 DAYS_BIRTH 0.078239 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: EXT_SOURCE_3 -0.178919 EXT_SOURCE_2 -0.160472 EXT_SOURCE_1 -0.155317 DAYS_EMPLOYED -0.044932 FLOORSMAX_AVG -0.044003 FLOORSMAX_MEDI -0.043768 FLOORSMAX_MODE -0.043226 AMT_GOODS_PRICE -0.039645 REGION_POPULATION_RELATIVE -0.037227 ELEVATORS_AVG -0.034199 Name: TARGET, dtype: float64
plt.hist(datasets["application_train"]['DAYS_BIRTH'] / -365, edgecolor = 'k', bins = 25)
plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');
sns.countplot(x='OCCUPATION_TYPE', data=datasets["application_train"]);
plt.title('Applicants Occupation');
plt.xticks(rotation=90);
df_train = datasets["application_train"]
import seaborn as sns
# IGNORE Warnings
import warnings
warnings.filterwarnings("ignore")
categorical_attributes = ['TARGET', 'CODE_GENDER', 'FLAG_OWN_REALTY', 'FLAG_OWN_CAR', 'NAME_CONTRACT_TYPE',
'NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'NAME_INCOME_TYPE']
fig, axes = plt.subplots(2, 4, figsize=(30, 20))
plt.subplots_adjust(left=None, bottom=None, right=None,
top=None, wspace=None, hspace=0.45)
plot_number = 0
for i in range(0, 2):
for j in range(0, 4):
current_plot = sns.countplot(x=categorical_attributes[plot_number],
data=df_train, hue='TARGET', ax=axes[i][j])
current_plot.set_title(f"Distribution of the {categorical_attributes[plot_number]} Variable")
current_plot.set_xticklabels(current_plot.get_xticklabels(), rotation=25)
plot_number += 1
#Important Categorical Features
sns.set(style="darkgrid")
fig, axs = plt.subplots(2, 2, figsize=(10, 8))
def add_data_labels(ax):
total = float(len(df_train))
for p in ax.patches:
count = p.get_height()
percentage = '{:.1f}%'.format(100 * count / total)
ax.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', fontsize=10, color='black', xytext=(0, 5), textcoords='offset points')
sns.histplot(data=df_train, x="NAME_CONTRACT_TYPE", ax=axs[0, 0], color='green')
add_data_labels(axs[0, 0])
sns.histplot(data=df_train, x="CODE_GENDER", ax=axs[0, 1], color='red')
add_data_labels(axs[0, 1])
sns.histplot(data=df_train, x="FLAG_OWN_CAR", ax=axs[1, 0], color='blue')
add_data_labels(axs[1, 0])
sns.histplot(data=df_train, x="FLAG_OWN_REALTY", ax=axs[1, 1], color='yellow')
add_data_labels(axs[1, 1])
plt.show()
run_analysis = True # Set this to True if you would like to run, else set it to False
if run_analysis:
numerical_attributes = ['TARGET', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'DAYS_EMPLOYED',
'DAYS_BIRTH', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'AMT_GOODS_PRICE']
df_subset = df_train[numerical_attributes]
df_subset['TARGET'].replace({0: "No Default", 1: "Default"}, inplace=True)
df_subset.fillna(0, inplace=True)
sns.pairplot(df_subset, hue="TARGET")
# Exclude non-numeric columns from correlation calculation
numeric_columns = df_train.select_dtypes(include=[np.number]).columns.tolist()
correlations = df_train[numeric_columns].corr()["TARGET"].sort_values()
print('Most Positive Correlations:\n', correlations.tail(10))
print('\nMost Negative Correlations:\n', correlations.head(10))
Most Positive Correlations: FLAG_DOCUMENT_3 0.044346 REG_CITY_NOT_LIVE_CITY 0.044395 FLAG_EMP_PHONE 0.045982 REG_CITY_NOT_WORK_CITY 0.050994 DAYS_ID_PUBLISH 0.051457 DAYS_LAST_PHONE_CHANGE 0.055218 REGION_RATING_CLIENT 0.058899 REGION_RATING_CLIENT_W_CITY 0.060893 DAYS_BIRTH 0.078239 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: EXT_SOURCE_3 -0.178919 EXT_SOURCE_2 -0.160472 EXT_SOURCE_1 -0.155317 DAYS_EMPLOYED -0.044932 FLOORSMAX_AVG -0.044003 FLOORSMAX_MEDI -0.043768 FLOORSMAX_MODE -0.043226 AMT_GOODS_PRICE -0.039645 REGION_POPULATION_RELATIVE -0.037227 ELEVATORS_AVG -0.034199 Name: TARGET, dtype: float64
#Correlation Matrix for a few numerical variables
correlation_data = df_train[['TARGET', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR']]
correlation_matrix = correlation_data.corr()
plt.figure(figsize=(16, 14))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Plot')
plt.show()
numerical_attributes = ['TARGET', 'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'DAYS_EMPLOYED',
'DAYS_BIRTH', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'AMT_GOODS_PRICE']
df_numerical = df_train[numerical_attributes]
correlation_matrix = df_numerical.corr()
correlation_matrix.style.background_gradient(cmap='coolwarm').set_precision(2)
| TARGET | AMT_INCOME_TOTAL | AMT_CREDIT | DAYS_EMPLOYED | DAYS_BIRTH | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | AMT_GOODS_PRICE | |
|---|---|---|---|---|---|---|---|---|---|
| TARGET | 1.00 | -0.00 | -0.03 | -0.04 | 0.08 | -0.16 | -0.16 | -0.18 | -0.04 |
| AMT_INCOME_TOTAL | -0.00 | 1.00 | 0.16 | -0.06 | 0.03 | 0.03 | 0.06 | -0.03 | 0.16 |
| AMT_CREDIT | -0.03 | 0.16 | 1.00 | -0.07 | -0.06 | 0.17 | 0.13 | 0.04 | 0.99 |
| DAYS_EMPLOYED | -0.04 | -0.06 | -0.07 | 1.00 | -0.62 | 0.29 | -0.02 | 0.11 | -0.06 |
| DAYS_BIRTH | 0.08 | 0.03 | -0.06 | -0.62 | 1.00 | -0.60 | -0.09 | -0.21 | -0.05 |
| EXT_SOURCE_1 | -0.16 | 0.03 | 0.17 | 0.29 | -0.60 | 1.00 | 0.21 | 0.19 | 0.18 |
| EXT_SOURCE_2 | -0.16 | 0.06 | 0.13 | -0.02 | -0.09 | 0.21 | 1.00 | 0.11 | 0.14 |
| EXT_SOURCE_3 | -0.18 | -0.03 | 0.04 | 0.11 | -0.21 | 0.19 | 0.11 | 1.00 | 0.05 |
| AMT_GOODS_PRICE | -0.04 | 0.16 | 0.99 | -0.06 | -0.05 | 0.18 | 0.14 | 0.05 | 1.00 |
pip install cufflinks
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Building wheels for collected packages: cufflinks
Building wheel for cufflinks (setup.py) ... done
Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=67918 sha256=00c43ea05d626043a3d24e2f1dfb877eca009674bcef5296e16b0546a3095e9a
Stored in directory: /root/.cache/pip/wheels/29/b4/f8/2fd2206eeeba6ccad8167e4e8894b8c4ec27bf1342037fd136
Successfully built cufflinks
Installing collected packages: tenacity, plotly, colorlover, cufflinks
Successfully installed colorlover-0.3.0 cufflinks-0.17.3 plotly-5.18.0 tenacity-8.2.3
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.3.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
pip install chart_studio
Collecting chart_studio
Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)
|████████████████████████████████| 64 kB 369 kB/s
Requirement already satisfied: plotly in /usr/local/lib/python3.9/site-packages (from chart_studio) (5.18.0)
Collecting retrying>=1.3.3
Downloading retrying-1.3.4-py3-none-any.whl (11 kB)
Requirement already satisfied: requests in /usr/local/lib/python3.9/site-packages (from chart_studio) (2.26.0)
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Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests->chart_studio) (2021.10.8)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/site-packages (from requests->chart_studio) (2.0.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests->chart_studio) (1.26.7)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.9/site-packages (from packaging->plotly->chart_studio) (3.0.6)
Installing collected packages: retrying, chart-studio
Successfully installed chart-studio-1.1.0 retrying-1.3.4
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.3.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
pip install --upgrade pandas cufflinks
Requirement already satisfied: pandas in /usr/local/lib/python3.9/site-packages (1.3.5)
Collecting pandas
Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)
|████████████████████████████████| 12.3 MB 1.9 MB/s
Requirement already satisfied: cufflinks in /usr/local/lib/python3.9/site-packages (0.17.3)
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Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/site-packages (from pandas) (2021.3)
Collecting tzdata>=2022.1
Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB)
|████████████████████████████████| 341 kB 2.7 MB/s
Collecting numpy<2,>=1.22.4
Downloading numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
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Requirement already satisfied: setuptools>=34.4.1 in /usr/local/lib/python3.9/site-packages (from cufflinks) (60.5.0)
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Requirement already satisfied: colorlover>=0.2.1 in /usr/local/lib/python3.9/site-packages (from cufflinks) (0.3.0)
Requirement already satisfied: plotly>=4.1.1 in /usr/local/lib/python3.9/site-packages (from cufflinks) (5.18.0)
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Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.9/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.8.0)
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Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.0.1)
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Requirement already satisfied: bleach in /usr/local/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (4.1.0)
Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.4)
Requirement already satisfied: defusedxml in /usr/local/lib/python3.9/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.7.1)
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Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.9/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.14.6)
Requirement already satisfied: webencodings in /usr/local/lib/python3.9/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)
Requirement already satisfied: pycparser in /usr/local/lib/python3.9/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.21)
Installing collected packages: tzdata, numpy, pandas
Attempting uninstall: numpy
Found existing installation: numpy 1.22.0
Uninstalling numpy-1.22.0:
Successfully uninstalled numpy-1.22.0
Attempting uninstall: pandas
Found existing installation: pandas 1.3.5
Uninstalling pandas-1.3.5:
Successfully uninstalled pandas-1.3.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
scipy 1.7.3 requires numpy<1.23.0,>=1.16.5, but you have numpy 1.26.2 which is incompatible.
basemap 1.3.0 requires numpy<1.22,>=1.16; python_version >= "3.5", but you have numpy 1.26.2 which is incompatible.
Successfully installed numpy-1.26.2 pandas-2.1.3 tzdata-2023.3
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: You are using pip version 21.3.1; however, version 23.3.1 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
import plotly.express as px
contract_type_counts = df_train['NAME_CONTRACT_TYPE'].value_counts()
contract_type_df = pd.DataFrame({'labels': contract_type_counts.index,
'values': contract_type_counts.values
})
fig = px.pie(contract_type_df, names='labels', values='values', title='Distribution of Loan Types')
fig.update_traces(hole=0.6)
fig.show()
pip install --upgrade plotly
Requirement already satisfied: plotly in /usr/local/lib/python3.9/site-packages (5.18.0) Requirement already satisfied: packaging in /usr/local/lib/python3.9/site-packages (from plotly) (21.3) Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/site-packages (from plotly) (8.2.3) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.9/site-packages (from packaging->plotly) (3.0.6) WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv WARNING: You are using pip version 21.3.1; however, version 23.3.1 is available. You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command. Note: you may need to restart the kernel to use updated packages.
import matplotlib.pyplot as plt
income_filter = df_train[df_train['AMT_INCOME_TOTAL'] < 2000000]
plt.figure(figsize=(10, 6))
plt.hist(income_filter['AMT_INCOME_TOTAL'], bins=100, color='blue', edgecolor='black')
plt.title('Distribution of Income (Filtered)')
plt.xlabel('Total Income')
plt.ylabel('Count of Applicants')
plt.show()
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 6))
plt.hist(df_train['AMT_CREDIT'], bins=100, color='green', edgecolor='black')
plt.title('Distribution of Credit Amount')
plt.xlabel('Credit Amount')
plt.ylabel('Count of Applicants')
plt.show()
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming df_train is your DataFrame containing the HCDR dataset
# Set a custom color palette for the plot
custom_palette = sns.color_palette("Set2")
# Set up the figure and axes
plt.figure(figsize=(12, 8))
# Box plot for 'AMT_CREDIT' with 'NAME_CONTRACT_TYPE' as huehttp://localhost:8888/notebooks/Courses/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/HCDR_baseLine_submission_with_numerical_and_cat_features_to_kaggle.ipynb#Summary-of-the-Applications-Dataset-and-Missing-Data
sns.boxplot(x='NAME_CONTRACT_TYPE', y='AMT_CREDIT', hue='CODE_GENDER', data=df_train, palette=custom_palette)
# Set plot labels and title
plt.xlabel('Contract Type')
plt.ylabel('Credit Amount')
plt.title('Box Plot of Credit Amount by Contract Type and Gender')
# Customize legend
plt.legend(title='Gender')
# Show the plot
plt.show()
prevApp = datasets['previous_application']
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["previous_application"], "previous_application")
display_feature_info(datasets["previous_application"], "previous_application")
--------------------------------------------------------------------------------
previous_application
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 SK_ID_PREV 1670214 non-null int64
1 SK_ID_CURR 1670214 non-null int64
2 NAME_CONTRACT_TYPE 1670214 non-null object
3 AMT_ANNUITY 1297979 non-null float64
4 AMT_APPLICATION 1670214 non-null float64
5 AMT_CREDIT 1670213 non-null float64
6 AMT_DOWN_PAYMENT 774370 non-null float64
7 AMT_GOODS_PRICE 1284699 non-null float64
8 WEEKDAY_APPR_PROCESS_START 1670214 non-null object
9 HOUR_APPR_PROCESS_START 1670214 non-null int64
10 FLAG_LAST_APPL_PER_CONTRACT 1670214 non-null object
11 NFLAG_LAST_APPL_IN_DAY 1670214 non-null int64
12 RATE_DOWN_PAYMENT 774370 non-null float64
13 RATE_INTEREST_PRIMARY 5951 non-null float64
14 RATE_INTEREST_PRIVILEGED 5951 non-null float64
15 NAME_CASH_LOAN_PURPOSE 1670214 non-null object
16 NAME_CONTRACT_STATUS 1670214 non-null object
17 DAYS_DECISION 1670214 non-null int64
18 NAME_PAYMENT_TYPE 1670214 non-null object
19 CODE_REJECT_REASON 1670214 non-null object
20 NAME_TYPE_SUITE 849809 non-null object
21 NAME_CLIENT_TYPE 1670214 non-null object
22 NAME_GOODS_CATEGORY 1670214 non-null object
23 NAME_PORTFOLIO 1670214 non-null object
24 NAME_PRODUCT_TYPE 1670214 non-null object
25 CHANNEL_TYPE 1670214 non-null object
26 SELLERPLACE_AREA 1670214 non-null int64
27 NAME_SELLER_INDUSTRY 1670214 non-null object
28 CNT_PAYMENT 1297984 non-null float64
29 NAME_YIELD_GROUP 1670214 non-null object
30 PRODUCT_COMBINATION 1669868 non-null object
31 DAYS_FIRST_DRAWING 997149 non-null float64
32 DAYS_FIRST_DUE 997149 non-null float64
33 DAYS_LAST_DUE_1ST_VERSION 997149 non-null float64
34 DAYS_LAST_DUE 997149 non-null float64
35 DAYS_TERMINATION 997149 non-null float64
36 NFLAG_INSURED_ON_APPROVAL 997149 non-null float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None
---------------------------------------------------------------------------
Shape of the df previous_application is (1670214, 37)
---------------------------------------------------------------------------
Statistical summary of previous_application is :
---------------------------------------------------------------------------
Description of the df previous_application:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for previous_application:
---------------------------------------------------------------------------
Data type value counts:
object 16
float64 15
int64 6
Name: count, dtype: int64
Return the number of unique elements in the object.
NAME_CONTRACT_TYPE 4
WEEKDAY_APPR_PROCESS_START 7
FLAG_LAST_APPL_PER_CONTRACT 2
NAME_CASH_LOAN_PURPOSE 25
NAME_CONTRACT_STATUS 4
NAME_PAYMENT_TYPE 4
CODE_REJECT_REASON 9
NAME_TYPE_SUITE 7
NAME_CLIENT_TYPE 4
NAME_GOODS_CATEGORY 28
NAME_PORTFOLIO 5
NAME_PRODUCT_TYPE 3
CHANNEL_TYPE 8
NAME_SELLER_INDUSTRY 11
NAME_YIELD_GROUP 5
PRODUCT_COMBINATION 17
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of previous_application.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_PREV', 'SK_ID_CURR', 'HOUR_APPR_PROCESS_START',
'NFLAG_LAST_APPL_IN_DAY', 'DAYS_DECISION', 'SELLERPLACE_AREA'],
dtype='object')}
------------------------------
{'float64': Index(['AMT_ANNUITY', 'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT',
'AMT_GOODS_PRICE', 'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
'RATE_INTEREST_PRIVILEGED', 'CNT_PAYMENT', 'DAYS_FIRST_DRAWING',
'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION', 'DAYS_LAST_DUE',
'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
dtype='object')}
------------------------------
{'object': Index(['NAME_CONTRACT_TYPE', 'WEEKDAY_APPR_PROCESS_START',
'FLAG_LAST_APPL_PER_CONTRACT', 'NAME_CASH_LOAN_PURPOSE',
'NAME_CONTRACT_STATUS', 'NAME_PAYMENT_TYPE', 'CODE_REJECT_REASON',
'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE', 'NAME_GOODS_CATEGORY',
'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE', 'CHANNEL_TYPE',
'NAME_SELLER_INDUSTRY', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION'],
dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| RATE_INTEREST_PRIVILEGED | 99.64 | 1664263 |
| RATE_INTEREST_PRIMARY | 99.64 | 1664263 |
| AMT_DOWN_PAYMENT | 53.64 | 895844 |
| RATE_DOWN_PAYMENT | 53.64 | 895844 |
| NAME_TYPE_SUITE | 49.12 | 820405 |
| NFLAG_INSURED_ON_APPROVAL | 40.30 | 673065 |
| DAYS_TERMINATION | 40.30 | 673065 |
| DAYS_LAST_DUE | 40.30 | 673065 |
| DAYS_LAST_DUE_1ST_VERSION | 40.30 | 673065 |
| DAYS_FIRST_DUE | 40.30 | 673065 |
| DAYS_FIRST_DRAWING | 40.30 | 673065 |
| AMT_GOODS_PRICE | 23.08 | 385515 |
| AMT_ANNUITY | 22.29 | 372235 |
| CNT_PAYMENT | 22.29 | 372230 |
| PRODUCT_COMBINATION | 0.02 | 346 |
---------------------------------------------------------------------------
display_stats(datasets['previous_application'], 'previous_application')
--------------------------------------------------------------------------------
previous_application
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 SK_ID_PREV 1670214 non-null int64
1 SK_ID_CURR 1670214 non-null int64
2 NAME_CONTRACT_TYPE 1670214 non-null object
3 AMT_ANNUITY 1297979 non-null float64
4 AMT_APPLICATION 1670214 non-null float64
5 AMT_CREDIT 1670213 non-null float64
6 AMT_DOWN_PAYMENT 774370 non-null float64
7 AMT_GOODS_PRICE 1284699 non-null float64
8 WEEKDAY_APPR_PROCESS_START 1670214 non-null object
9 HOUR_APPR_PROCESS_START 1670214 non-null int64
10 FLAG_LAST_APPL_PER_CONTRACT 1670214 non-null object
11 NFLAG_LAST_APPL_IN_DAY 1670214 non-null int64
12 RATE_DOWN_PAYMENT 774370 non-null float64
13 RATE_INTEREST_PRIMARY 5951 non-null float64
14 RATE_INTEREST_PRIVILEGED 5951 non-null float64
15 NAME_CASH_LOAN_PURPOSE 1670214 non-null object
16 NAME_CONTRACT_STATUS 1670214 non-null object
17 DAYS_DECISION 1670214 non-null int64
18 NAME_PAYMENT_TYPE 1670214 non-null object
19 CODE_REJECT_REASON 1670214 non-null object
20 NAME_TYPE_SUITE 849809 non-null object
21 NAME_CLIENT_TYPE 1670214 non-null object
22 NAME_GOODS_CATEGORY 1670214 non-null object
23 NAME_PORTFOLIO 1670214 non-null object
24 NAME_PRODUCT_TYPE 1670214 non-null object
25 CHANNEL_TYPE 1670214 non-null object
26 SELLERPLACE_AREA 1670214 non-null int64
27 NAME_SELLER_INDUSTRY 1670214 non-null object
28 CNT_PAYMENT 1297984 non-null float64
29 NAME_YIELD_GROUP 1670214 non-null object
30 PRODUCT_COMBINATION 1669868 non-null object
31 DAYS_FIRST_DRAWING 997149 non-null float64
32 DAYS_FIRST_DUE 997149 non-null float64
33 DAYS_LAST_DUE_1ST_VERSION 997149 non-null float64
34 DAYS_LAST_DUE 997149 non-null float64
35 DAYS_TERMINATION 997149 non-null float64
36 NFLAG_INSURED_ON_APPROVAL 997149 non-null float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None
---------------------------------------------------------------------------
Shape of the df previous_application is (1670214, 37)
---------------------------------------------------------------------------
Statistical summary of previous_application is :
---------------------------------------------------------------------------
Description of the df previous_application:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
The number of children reaches as high as 19, which suggests a potential outlier that warrants further investigation.
Additionally, the presence of negative values in all the "number of day" fields indicates anomalies in the data.
Nevertheless, certain fields provide information on average years. Conducting a calculation to compare the average years with the corresponding days could yield valuable insights.
prevApp.columns
Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
dtype='object')
#Important Categorical Features
sns.set(style="darkgrid")
fig, axs = plt.subplots(2, 2, figsize=(16, 10))
def add_data_labels(ax):
total = float(len(prevApp))
for p in ax.patches:
count = p.get_height()
percentage = '{:.1f}%'.format(100 * count / total)
ax.annotate(f'{count} ({percentage})', (p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center', fontsize=10, color='black', xytext=(0, 5), textcoords='offset points')
sns.histplot(data=prevApp, x="NAME_CONTRACT_TYPE", ax=axs[0, 0], color='green')
add_data_labels(axs[0, 0])
sns.histplot(data=prevApp, x="NAME_CONTRACT_STATUS", ax=axs[0, 1], color='red')
add_data_labels(axs[0, 1])
sns.histplot(data=prevApp, x="NAME_YIELD_GROUP", ax=axs[1, 0], color='blue')
add_data_labels(axs[1, 0])
sns.histplot(data=prevApp, x="NAME_PORTFOLIO", ax=axs[1, 1], color='yellow')
add_data_labels(axs[1, 1])
plt.show()
# Correlation Plot between important features
correlation_data = prevApp[['AMT_ANNUITY', 'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE', 'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY', 'RATE_INTEREST_PRIVILEGED', 'CNT_PAYMENT']]
correlation_matrix = correlation_data.corr()
plt.figure(figsize=(16, 12))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Plot for Previous Application')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["bureau"], "bureau")
display_feature_info(datasets["bureau"], "bureau")
--------------------------------------------------------------------------------
bureau
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
# Column Dtype
--- ------ -----
0 SK_ID_CURR int64
1 SK_ID_BUREAU int64
2 CREDIT_ACTIVE object
3 CREDIT_CURRENCY object
4 DAYS_CREDIT int64
5 CREDIT_DAY_OVERDUE int64
6 DAYS_CREDIT_ENDDATE float64
7 DAYS_ENDDATE_FACT float64
8 AMT_CREDIT_MAX_OVERDUE float64
9 CNT_CREDIT_PROLONG int64
10 AMT_CREDIT_SUM float64
11 AMT_CREDIT_SUM_DEBT float64
12 AMT_CREDIT_SUM_LIMIT float64
13 AMT_CREDIT_SUM_OVERDUE float64
14 CREDIT_TYPE object
15 DAYS_CREDIT_UPDATE int64
16 AMT_ANNUITY float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None
---------------------------------------------------------------------------
Shape of the df bureau is (1716428, 17)
---------------------------------------------------------------------------
Statistical summary of bureau is :
---------------------------------------------------------------------------
Description of the df bureau:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for bureau:
---------------------------------------------------------------------------
Data type value counts:
float64 8
int64 6
object 3
Name: count, dtype: int64
Return the number of unique elements in the object.
CREDIT_ACTIVE 4
CREDIT_CURRENCY 4
CREDIT_TYPE 15
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of bureau.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_CURR', 'SK_ID_BUREAU', 'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE',
'CNT_CREDIT_PROLONG', 'DAYS_CREDIT_UPDATE'],
dtype='object')}
------------------------------
{'float64': Index(['DAYS_CREDIT_ENDDATE', 'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE',
'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
'AMT_CREDIT_SUM_OVERDUE', 'AMT_ANNUITY'],
dtype='object')}
------------------------------
{'object': Index(['CREDIT_ACTIVE', 'CREDIT_CURRENCY', 'CREDIT_TYPE'], dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| AMT_ANNUITY | 71.47 | 1226791 |
| AMT_CREDIT_MAX_OVERDUE | 65.51 | 1124488 |
| DAYS_ENDDATE_FACT | 36.92 | 633653 |
| AMT_CREDIT_SUM_LIMIT | 34.48 | 591780 |
| AMT_CREDIT_SUM_DEBT | 15.01 | 257669 |
| DAYS_CREDIT_ENDDATE | 6.15 | 105553 |
---------------------------------------------------------------------------
display_stats(datasets['bureau'], 'bureau')
--------------------------------------------------------------------------------
bureau
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
# Column Dtype
--- ------ -----
0 SK_ID_CURR int64
1 SK_ID_BUREAU int64
2 CREDIT_ACTIVE object
3 CREDIT_CURRENCY object
4 DAYS_CREDIT int64
5 CREDIT_DAY_OVERDUE int64
6 DAYS_CREDIT_ENDDATE float64
7 DAYS_ENDDATE_FACT float64
8 AMT_CREDIT_MAX_OVERDUE float64
9 CNT_CREDIT_PROLONG int64
10 AMT_CREDIT_SUM float64
11 AMT_CREDIT_SUM_DEBT float64
12 AMT_CREDIT_SUM_LIMIT float64
13 AMT_CREDIT_SUM_OVERDUE float64
14 CREDIT_TYPE object
15 DAYS_CREDIT_UPDATE int64
16 AMT_ANNUITY float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None
---------------------------------------------------------------------------
Shape of the df bureau is (1716428, 17)
---------------------------------------------------------------------------
Statistical summary of bureau is :
---------------------------------------------------------------------------
Description of the df bureau:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
datasets['bureau'].columns
Index(['SK_ID_CURR', 'SK_ID_BUREAU', 'CREDIT_ACTIVE', 'CREDIT_CURRENCY',
'DAYS_CREDIT', 'CREDIT_DAY_OVERDUE', 'DAYS_CREDIT_ENDDATE',
'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'CNT_CREDIT_PROLONG',
'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT',
'AMT_CREDIT_SUM_OVERDUE', 'CREDIT_TYPE', 'DAYS_CREDIT_UPDATE',
'AMT_ANNUITY'],
dtype='object')
datasets['bureau'].describe()
| SK_ID_CURR | SK_ID_BUREAU | DAYS_CREDIT | CREDIT_DAY_OVERDUE | DAYS_CREDIT_ENDDATE | DAYS_ENDDATE_FACT | AMT_CREDIT_MAX_OVERDUE | CNT_CREDIT_PROLONG | AMT_CREDIT_SUM | AMT_CREDIT_SUM_DEBT | AMT_CREDIT_SUM_LIMIT | AMT_CREDIT_SUM_OVERDUE | DAYS_CREDIT_UPDATE | AMT_ANNUITY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.716428e+06 | 1.716428e+06 | 1.716428e+06 | 1.716428e+06 | 1.610875e+06 | 1.082775e+06 | 5.919400e+05 | 1.716428e+06 | 1.716415e+06 | 1.458759e+06 | 1.124648e+06 | 1.716428e+06 | 1.716428e+06 | 4.896370e+05 |
| mean | 2.782149e+05 | 5.924434e+06 | -1.142108e+03 | 8.181666e-01 | 5.105174e+02 | -1.017437e+03 | 3.825418e+03 | 6.410406e-03 | 3.549946e+05 | 1.370851e+05 | 6.229515e+03 | 3.791276e+01 | -5.937483e+02 | 1.571276e+04 |
| std | 1.029386e+05 | 5.322657e+05 | 7.951649e+02 | 3.654443e+01 | 4.994220e+03 | 7.140106e+02 | 2.060316e+05 | 9.622391e-02 | 1.149811e+06 | 6.774011e+05 | 4.503203e+04 | 5.937650e+03 | 7.207473e+02 | 3.258269e+05 |
| min | 1.000010e+05 | 5.000000e+06 | -2.922000e+03 | 0.000000e+00 | -4.206000e+04 | -4.202300e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | -4.705600e+06 | -5.864061e+05 | 0.000000e+00 | -4.194700e+04 | 0.000000e+00 |
| 25% | 1.888668e+05 | 5.463954e+06 | -1.666000e+03 | 0.000000e+00 | -1.138000e+03 | -1.489000e+03 | 0.000000e+00 | 0.000000e+00 | 5.130000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | -9.080000e+02 | 0.000000e+00 |
| 50% | 2.780550e+05 | 5.926304e+06 | -9.870000e+02 | 0.000000e+00 | -3.300000e+02 | -8.970000e+02 | 0.000000e+00 | 0.000000e+00 | 1.255185e+05 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | -3.950000e+02 | 0.000000e+00 |
| 75% | 3.674260e+05 | 6.385681e+06 | -4.740000e+02 | 0.000000e+00 | 4.740000e+02 | -4.250000e+02 | 0.000000e+00 | 0.000000e+00 | 3.150000e+05 | 4.015350e+04 | 0.000000e+00 | 0.000000e+00 | -3.300000e+01 | 1.350000e+04 |
| max | 4.562550e+05 | 6.843457e+06 | 0.000000e+00 | 2.792000e+03 | 3.119900e+04 | 0.000000e+00 | 1.159872e+08 | 9.000000e+00 | 5.850000e+08 | 1.701000e+08 | 4.705600e+06 | 3.756681e+06 | 3.720000e+02 | 1.184534e+08 |
datasets['bureau'].info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1716428 entries, 0 to 1716427 Data columns (total 17 columns): # Column Dtype --- ------ ----- 0 SK_ID_CURR int64 1 SK_ID_BUREAU int64 2 CREDIT_ACTIVE object 3 CREDIT_CURRENCY object 4 DAYS_CREDIT int64 5 CREDIT_DAY_OVERDUE int64 6 DAYS_CREDIT_ENDDATE float64 7 DAYS_ENDDATE_FACT float64 8 AMT_CREDIT_MAX_OVERDUE float64 9 CNT_CREDIT_PROLONG int64 10 AMT_CREDIT_SUM float64 11 AMT_CREDIT_SUM_DEBT float64 12 AMT_CREDIT_SUM_LIMIT float64 13 AMT_CREDIT_SUM_OVERDUE float64 14 CREDIT_TYPE object 15 DAYS_CREDIT_UPDATE int64 16 AMT_ANNUITY float64 dtypes: float64(8), int64(6), object(3) memory usage: 222.6+ MB
bureau = datasets['bureau']
#Important Categorical Features
sns.set(style="darkgrid")
fig, ax = plt.subplots(figsize=(10, 8))
sns.histplot(data=bureau, x="CREDIT_ACTIVE", ax=ax, color='green')
add_data_labels(ax)
plt.show()
numerical_columns = bureau.select_dtypes(include=['float64', 'int64']).columns
numerical_data = bureau[numerical_columns]
numerical_data.hist(bins=50, figsize=(20,15))
plt.show()
# Correlation Plot
correlation_data = bureau[['DAYS_CREDIT','CREDIT_DAY_OVERDUE','DAYS_CREDIT_ENDDATE', 'DAYS_ENDDATE_FACT', 'AMT_CREDIT_MAX_OVERDUE', 'AMT_CREDIT_SUM', 'AMT_CREDIT_SUM_DEBT', 'AMT_CREDIT_SUM_LIMIT', 'AMT_CREDIT_SUM_OVERDUE']]
correlation_matrix = correlation_data.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Plot for Bureau')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["bureau_balance"], "bureau_balance")
display_feature_info(datasets["bureau_balance"], "bureau_balance")
--------------------------------------------------------------------------------
bureau_balance
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
# Column Dtype
--- ------ -----
0 SK_ID_BUREAU int64
1 MONTHS_BALANCE int64
2 STATUS object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
None
---------------------------------------------------------------------------
Shape of the df bureau_balance is (27299925, 3)
---------------------------------------------------------------------------
Statistical summary of bureau_balance is :
---------------------------------------------------------------------------
Description of the df bureau_balance:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for bureau_balance:
---------------------------------------------------------------------------
Data type value counts:
int64 2
object 1
Name: count, dtype: int64
Return the number of unique elements in the object.
STATUS 8
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of bureau_balance.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_BUREAU', 'MONTHS_BALANCE'], dtype='object')}
------------------------------
{'object': Index(['STATUS'], dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
No missing Data
Bureau balance & bureau have no missing data. These datasets can provide accurate aggreagte features.
bureauBal = datasets["bureau_balance"]
bureauBal.head(5)
| SK_ID_BUREAU | MONTHS_BALANCE | STATUS | |
|---|---|---|---|
| 0 | 5715448 | 0 | C |
| 1 | 5715448 | -1 | C |
| 2 | 5715448 | -2 | C |
| 3 | 5715448 | -3 | C |
| 4 | 5715448 | -4 | C |
bureauBal.columns
Index(['SK_ID_BUREAU', 'MONTHS_BALANCE', 'STATUS'], dtype='object')
bureauBal.describe()
| SK_ID_BUREAU | MONTHS_BALANCE | |
|---|---|---|
| count | 2.729992e+07 | 2.729992e+07 |
| mean | 6.036297e+06 | -3.074169e+01 |
| std | 4.923489e+05 | 2.386451e+01 |
| min | 5.001709e+06 | -9.600000e+01 |
| 25% | 5.730933e+06 | -4.600000e+01 |
| 50% | 6.070821e+06 | -2.500000e+01 |
| 75% | 6.431951e+06 | -1.100000e+01 |
| max | 6.842888e+06 | 0.000000e+00 |
bureauBal.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 27299925 entries, 0 to 27299924 Data columns (total 3 columns): # Column Dtype --- ------ ----- 0 SK_ID_BUREAU int64 1 MONTHS_BALANCE int64 2 STATUS object dtypes: int64(2), object(1) memory usage: 624.8+ MB
#Important Categorical Feature
plt.figure(figsize=(10,5))
sns.set_theme()
sns.countplot(x = 'STATUS',data = bureauBal)
plt.xlabel("Status",fontweight='bold',size=13)
plt.ylabel("Count",fontweight='bold',size=13)
plt.show()
# Box plot to visualize the relationship between STATUS and Months Balance
plt.figure(figsize=(10, 6))
sns.boxplot(x='STATUS', y='MONTHS_BALANCE', data=bureauBal, order=['0', 'C', '1', '2', '3', '4', '5'])
plt.title('Box Plot of Months Balance by STATUS')
plt.xlabel('STATUS')
plt.ylabel('Months Balance')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["credit_card_balance"], "credit_card_balance")
display_feature_info(datasets["credit_card_balance"], "credit_card_balance")
--------------------------------------------------------------------------------
credit_card_balance
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
# Column Dtype
--- ------ -----
0 SK_ID_PREV int64
1 SK_ID_CURR int64
2 MONTHS_BALANCE int64
3 AMT_BALANCE float64
4 AMT_CREDIT_LIMIT_ACTUAL int64
5 AMT_DRAWINGS_ATM_CURRENT float64
6 AMT_DRAWINGS_CURRENT float64
7 AMT_DRAWINGS_OTHER_CURRENT float64
8 AMT_DRAWINGS_POS_CURRENT float64
9 AMT_INST_MIN_REGULARITY float64
10 AMT_PAYMENT_CURRENT float64
11 AMT_PAYMENT_TOTAL_CURRENT float64
12 AMT_RECEIVABLE_PRINCIPAL float64
13 AMT_RECIVABLE float64
14 AMT_TOTAL_RECEIVABLE float64
15 CNT_DRAWINGS_ATM_CURRENT float64
16 CNT_DRAWINGS_CURRENT int64
17 CNT_DRAWINGS_OTHER_CURRENT float64
18 CNT_DRAWINGS_POS_CURRENT float64
19 CNT_INSTALMENT_MATURE_CUM float64
20 NAME_CONTRACT_STATUS object
21 SK_DPD int64
22 SK_DPD_DEF int64
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
None
---------------------------------------------------------------------------
Shape of the df credit_card_balance is (3840312, 23)
---------------------------------------------------------------------------
Statistical summary of credit_card_balance is :
---------------------------------------------------------------------------
Description of the df credit_card_balance:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for credit_card_balance:
---------------------------------------------------------------------------
Data type value counts:
float64 15
int64 7
object 1
Name: count, dtype: int64
Return the number of unique elements in the object.
NAME_CONTRACT_STATUS 7
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of credit_card_balance.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'AMT_CREDIT_LIMIT_ACTUAL',
'CNT_DRAWINGS_CURRENT', 'SK_DPD', 'SK_DPD_DEF'],
dtype='object')}
------------------------------
{'float64': Index(['AMT_BALANCE', 'AMT_DRAWINGS_ATM_CURRENT', 'AMT_DRAWINGS_CURRENT',
'AMT_DRAWINGS_OTHER_CURRENT', 'AMT_DRAWINGS_POS_CURRENT',
'AMT_INST_MIN_REGULARITY', 'AMT_PAYMENT_CURRENT',
'AMT_PAYMENT_TOTAL_CURRENT', 'AMT_RECEIVABLE_PRINCIPAL',
'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE', 'CNT_DRAWINGS_ATM_CURRENT',
'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',
'CNT_INSTALMENT_MATURE_CUM'],
dtype='object')}
------------------------------
{'object': Index(['NAME_CONTRACT_STATUS'], dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| AMT_PAYMENT_CURRENT | 20.00 | 767988 |
| AMT_DRAWINGS_ATM_CURRENT | 19.52 | 749816 |
| CNT_DRAWINGS_POS_CURRENT | 19.52 | 749816 |
| AMT_DRAWINGS_OTHER_CURRENT | 19.52 | 749816 |
| AMT_DRAWINGS_POS_CURRENT | 19.52 | 749816 |
| CNT_DRAWINGS_OTHER_CURRENT | 19.52 | 749816 |
| CNT_DRAWINGS_ATM_CURRENT | 19.52 | 749816 |
| CNT_INSTALMENT_MATURE_CUM | 7.95 | 305236 |
| AMT_INST_MIN_REGULARITY | 7.95 | 305236 |
---------------------------------------------------------------------------
CCBalance = datasets["credit_card_balance"]
CCBalance.columns
Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'AMT_BALANCE',
'AMT_CREDIT_LIMIT_ACTUAL', 'AMT_DRAWINGS_ATM_CURRENT',
'AMT_DRAWINGS_CURRENT', 'AMT_DRAWINGS_OTHER_CURRENT',
'AMT_DRAWINGS_POS_CURRENT', 'AMT_INST_MIN_REGULARITY',
'AMT_PAYMENT_CURRENT', 'AMT_PAYMENT_TOTAL_CURRENT',
'AMT_RECEIVABLE_PRINCIPAL', 'AMT_RECIVABLE', 'AMT_TOTAL_RECEIVABLE',
'CNT_DRAWINGS_ATM_CURRENT', 'CNT_DRAWINGS_CURRENT',
'CNT_DRAWINGS_OTHER_CURRENT', 'CNT_DRAWINGS_POS_CURRENT',
'CNT_INSTALMENT_MATURE_CUM', 'NAME_CONTRACT_STATUS', 'SK_DPD',
'SK_DPD_DEF'],
dtype='object')
CCBalance.describe()
| SK_ID_PREV | SK_ID_CURR | MONTHS_BALANCE | AMT_BALANCE | AMT_CREDIT_LIMIT_ACTUAL | AMT_DRAWINGS_ATM_CURRENT | AMT_DRAWINGS_CURRENT | AMT_DRAWINGS_OTHER_CURRENT | AMT_DRAWINGS_POS_CURRENT | AMT_INST_MIN_REGULARITY | ... | AMT_RECEIVABLE_PRINCIPAL | AMT_RECIVABLE | AMT_TOTAL_RECEIVABLE | CNT_DRAWINGS_ATM_CURRENT | CNT_DRAWINGS_CURRENT | CNT_DRAWINGS_OTHER_CURRENT | CNT_DRAWINGS_POS_CURRENT | CNT_INSTALMENT_MATURE_CUM | SK_DPD | SK_DPD_DEF | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 3.840312e+06 | 3.840312e+06 | 3.840312e+06 | 3.840312e+06 | 3.840312e+06 | 3.090496e+06 | 3.840312e+06 | 3.090496e+06 | 3.090496e+06 | 3.535076e+06 | ... | 3.840312e+06 | 3.840312e+06 | 3.840312e+06 | 3.090496e+06 | 3.840312e+06 | 3.090496e+06 | 3.090496e+06 | 3.535076e+06 | 3.840312e+06 | 3.840312e+06 |
| mean | 1.904504e+06 | 2.783242e+05 | -3.452192e+01 | 5.830016e+04 | 1.538080e+05 | 5.961325e+03 | 7.433388e+03 | 2.881696e+02 | 2.968805e+03 | 3.540204e+03 | ... | 5.596588e+04 | 5.808881e+04 | 5.809829e+04 | 3.094490e-01 | 7.031439e-01 | 4.812496e-03 | 5.594791e-01 | 2.082508e+01 | 9.283667e+00 | 3.316220e-01 |
| std | 5.364695e+05 | 1.027045e+05 | 2.666775e+01 | 1.063070e+05 | 1.651457e+05 | 2.822569e+04 | 3.384608e+04 | 8.201989e+03 | 2.079689e+04 | 5.600154e+03 | ... | 1.025336e+05 | 1.059654e+05 | 1.059718e+05 | 1.100401e+00 | 3.190347e+00 | 8.263861e-02 | 3.240649e+00 | 2.005149e+01 | 9.751570e+01 | 2.147923e+01 |
| min | 1.000018e+06 | 1.000060e+05 | -9.600000e+01 | -4.202502e+05 | 0.000000e+00 | -6.827310e+03 | -6.211620e+03 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | -4.233058e+05 | -4.202502e+05 | -4.202502e+05 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 25% | 1.434385e+06 | 1.895170e+05 | -5.500000e+01 | 0.000000e+00 | 4.500000e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 4.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 50% | 1.897122e+06 | 2.783960e+05 | -2.800000e+01 | 0.000000e+00 | 1.125000e+05 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | ... | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 1.500000e+01 | 0.000000e+00 | 0.000000e+00 |
| 75% | 2.369328e+06 | 3.675800e+05 | -1.100000e+01 | 8.904669e+04 | 1.800000e+05 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 6.633911e+03 | ... | 8.535924e+04 | 8.889949e+04 | 8.891451e+04 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.200000e+01 | 0.000000e+00 | 0.000000e+00 |
| max | 2.843496e+06 | 4.562500e+05 | -1.000000e+00 | 1.505902e+06 | 1.350000e+06 | 2.115000e+06 | 2.287098e+06 | 1.529847e+06 | 2.239274e+06 | 2.028820e+05 | ... | 1.472317e+06 | 1.493338e+06 | 1.493338e+06 | 5.100000e+01 | 1.650000e+02 | 1.200000e+01 | 1.650000e+02 | 1.200000e+02 | 3.260000e+03 | 3.260000e+03 |
8 rows × 22 columns
CCBalance.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 3840312 entries, 0 to 3840311 Data columns (total 23 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 MONTHS_BALANCE int64 3 AMT_BALANCE float64 4 AMT_CREDIT_LIMIT_ACTUAL int64 5 AMT_DRAWINGS_ATM_CURRENT float64 6 AMT_DRAWINGS_CURRENT float64 7 AMT_DRAWINGS_OTHER_CURRENT float64 8 AMT_DRAWINGS_POS_CURRENT float64 9 AMT_INST_MIN_REGULARITY float64 10 AMT_PAYMENT_CURRENT float64 11 AMT_PAYMENT_TOTAL_CURRENT float64 12 AMT_RECEIVABLE_PRINCIPAL float64 13 AMT_RECIVABLE float64 14 AMT_TOTAL_RECEIVABLE float64 15 CNT_DRAWINGS_ATM_CURRENT float64 16 CNT_DRAWINGS_CURRENT int64 17 CNT_DRAWINGS_OTHER_CURRENT float64 18 CNT_DRAWINGS_POS_CURRENT float64 19 CNT_INSTALMENT_MATURE_CUM float64 20 NAME_CONTRACT_STATUS object 21 SK_DPD int64 22 SK_DPD_DEF int64 dtypes: float64(15), int64(7), object(1) memory usage: 673.9+ MB
#Testing Contract Status Variable
plt.figure(figsize=(10,5))
sns.set_theme()
sns.countplot(x = 'NAME_CONTRACT_STATUS',data = CCBalance)
plt.xlabel("Contract Status",fontweight='bold',size=13)
plt.ylabel("Number",fontweight='bold',size=13)
plt.show()
#Important Numerical Features
sns.set(style="darkgrid")
fig,axs=plt.subplots(2,2,figsize=(10,8))
sns.histplot(data=CCBalance,x="MONTHS_BALANCE",kde=True,ax=axs[0,0],color='green')
sns.histplot(data=CCBalance,x="AMT_BALANCE",kde=True,ax=axs[0,1],color='red')
sns.histplot(data=CCBalance,x="AMT_CREDIT_LIMIT_ACTUAL",kde=True,ax=axs[1,0],color='blue')
sns.histplot(data=CCBalance,x="AMT_DRAWINGS_CURRENT",kde=True,ax=axs[1,1],color='yellow')
<Axes: xlabel='AMT_DRAWINGS_CURRENT', ylabel='Count'>
numerical_columns = CCBalance.select_dtypes(include=['float64', 'int64']).columns
numerical_data = CCBalance[numerical_columns]
numerical_data.hist(bins=50, figsize=(20,15))
plt.show()
#Correlation between all vairables
exclude_variables = ['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_STATUS']
selected_variables = [col for col in CCBalance.columns if col not in exclude_variables]
correlation_data = CCBalance[selected_variables]
correlation_matrix = correlation_data.corr()
plt.figure(figsize=(16, 14))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Matrix for Cedit Card Balance')
plt.show()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["installments_payments"], "installments_payments")
display_feature_info(datasets["installments_payments"], "installments_payments")
--------------------------------------------------------------------------------
installments_payments
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
# Column Dtype
--- ------ -----
0 SK_ID_PREV int64
1 SK_ID_CURR int64
2 NUM_INSTALMENT_VERSION float64
3 NUM_INSTALMENT_NUMBER int64
4 DAYS_INSTALMENT float64
5 DAYS_ENTRY_PAYMENT float64
6 AMT_INSTALMENT float64
7 AMT_PAYMENT float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None
---------------------------------------------------------------------------
Shape of the df installments_payments is (13605401, 8)
---------------------------------------------------------------------------
Statistical summary of installments_payments is :
---------------------------------------------------------------------------
Description of the df installments_payments:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for installments_payments:
---------------------------------------------------------------------------
Data type value counts:
float64 5
int64 3
Name: count, dtype: int64
Return the number of unique elements in the object.
Series([], dtype: float64)
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of installments_payments.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_PREV', 'SK_ID_CURR', 'NUM_INSTALMENT_NUMBER'], dtype='object')}
------------------------------
{'float64': Index(['NUM_INSTALMENT_VERSION', 'DAYS_INSTALMENT', 'DAYS_ENTRY_PAYMENT',
'AMT_INSTALMENT', 'AMT_PAYMENT'],
dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| DAYS_ENTRY_PAYMENT | 0.02 | 2905 |
| AMT_PAYMENT | 0.02 | 2905 |
---------------------------------------------------------------------------
installPay = datasets["installments_payments"]
installPay.columns
Index(['SK_ID_PREV', 'SK_ID_CURR', 'NUM_INSTALMENT_VERSION',
'NUM_INSTALMENT_NUMBER', 'DAYS_INSTALMENT', 'DAYS_ENTRY_PAYMENT',
'AMT_INSTALMENT', 'AMT_PAYMENT'],
dtype='object')
installPay.describe()
| SK_ID_PREV | SK_ID_CURR | NUM_INSTALMENT_VERSION | NUM_INSTALMENT_NUMBER | DAYS_INSTALMENT | DAYS_ENTRY_PAYMENT | AMT_INSTALMENT | AMT_PAYMENT | |
|---|---|---|---|---|---|---|---|---|
| count | 1.360540e+07 | 1.360540e+07 | 1.360540e+07 | 1.360540e+07 | 1.360540e+07 | 1.360250e+07 | 1.360540e+07 | 1.360250e+07 |
| mean | 1.903365e+06 | 2.784449e+05 | 8.566373e-01 | 1.887090e+01 | -1.042270e+03 | -1.051114e+03 | 1.705091e+04 | 1.723822e+04 |
| std | 5.362029e+05 | 1.027183e+05 | 1.035216e+00 | 2.666407e+01 | 8.009463e+02 | 8.005859e+02 | 5.057025e+04 | 5.473578e+04 |
| min | 1.000001e+06 | 1.000010e+05 | 0.000000e+00 | 1.000000e+00 | -2.922000e+03 | -4.921000e+03 | 0.000000e+00 | 0.000000e+00 |
| 25% | 1.434191e+06 | 1.896390e+05 | 0.000000e+00 | 4.000000e+00 | -1.654000e+03 | -1.662000e+03 | 4.226085e+03 | 3.398265e+03 |
| 50% | 1.896520e+06 | 2.786850e+05 | 1.000000e+00 | 8.000000e+00 | -8.180000e+02 | -8.270000e+02 | 8.884080e+03 | 8.125515e+03 |
| 75% | 2.369094e+06 | 3.675300e+05 | 1.000000e+00 | 1.900000e+01 | -3.610000e+02 | -3.700000e+02 | 1.671021e+04 | 1.610842e+04 |
| max | 2.843499e+06 | 4.562550e+05 | 1.780000e+02 | 2.770000e+02 | -1.000000e+00 | -1.000000e+00 | 3.771488e+06 | 3.771488e+06 |
installPay.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 13605401 entries, 0 to 13605400 Data columns (total 8 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 NUM_INSTALMENT_VERSION float64 3 NUM_INSTALMENT_NUMBER int64 4 DAYS_INSTALMENT float64 5 DAYS_ENTRY_PAYMENT float64 6 AMT_INSTALMENT float64 7 AMT_PAYMENT float64 dtypes: float64(5), int64(3) memory usage: 830.4 MB
#Correlation between all vairables
exclude_variables = ['SK_ID_PREV', 'SK_ID_CURR']
selected_variables = [col for col in installPay.columns if col not in exclude_variables]
correlation_data = installPay[selected_variables]
correlation_matrix = correlation_data.corr()
plt.figure(figsize=(16, 14))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Matrix for Installment Payments')
plt.show()
sns.set(style="darkgrid")
fig,axs=plt.subplots(2,2,figsize=(10,8))
sns.histplot(data=installPay,x="NUM_INSTALMENT_NUMBER",kde=True,ax=axs[0,0],color='green')
sns.histplot(data=installPay,x="DAYS_INSTALMENT",kde=True,ax=axs[0,1],color='red')
sns.histplot(data=installPay,x="DAYS_ENTRY_PAYMENT",kde=True,ax=axs[1,0],color='blue')
<Axes: xlabel='DAYS_ENTRY_PAYMENT', ylabel='Count'>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display, HTML
# Assuming datasets is a DataFrame containing your data
def stats_summary1(df, df_name):
print(datasets[df_name].info(verbose=True))
print("-----" * 15)
print(f"Shape of the df {df_name} is {df.shape} \n")
print("-----" * 15)
print(f"Statistical summary of {df_name} is :")
print("-----" * 15)
print(f"Description of the df {df_name}:\n")
print(display(HTML(np.round(datasets['application_train'].describe(), 2).to_html())))
def stats_summary2(df, df_name):
print(f"Description of the df continued for {df_name}:\n")
print("-----" * 15)
print("Data type value counts: \n", df.dtypes.value_counts())
print("\nReturn the number of unique elements in the object. \n")
print(df.select_dtypes('object').apply(pd.Series.nunique, axis=0))
# List the categorical and Numerical features of a DF
def feature_datatypes_groups(df, df_name):
df_dtypes = df.columns.to_series().groupby(df.dtypes).groups
print("-----" * 15)
print(f"Categorical and Numerical(int + float) features of {df_name}.")
print("-----" * 15)
print()
for k, v in df_dtypes.items():
print({k.name: v})
print("---" * 10)
print("\n \n")
# Null data list and plot.
def null_data_plot(df, df_name):
percent = (df.isnull().sum() / df.isnull().count() * 100).sort_values(ascending=False).round(2)
sum_missing = df.isna().sum().sort_values(ascending=False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
print("-----" * 15)
print("-----" * 15)
print('\n The Missing Data: \n')
if len(missing_data) == 0:
print("No missing Data")
else:
display(HTML(missing_data.to_html())) # display all the rows
print("-----" * 15)
if len(df.columns) > 35:
f, ax = plt.subplots(figsize=(8, 15))
else:
f, ax = plt.subplots()
plt.title(f'Percent missing data for {df_name}.', fontsize=10)
fig = sns.barplot(x="Percent", y=missing_data.index, data=missing_data, alpha=0.8)
plt.xlabel('Percent of missing values', fontsize=10)
plt.ylabel('Features', fontsize=10)
return missing_data
# Full consolidation of all the stats function.
def display_stats(df, df_name):
print("--" * 40)
print(" " * 20 + '\033[1m' + df_name + '\033[0m' + " " * 20)
print("--" * 40)
stats_summary1(df, df_name)
def display_feature_info(df, df_name):
stats_summary2(df, df_name)
feature_datatypes_groups(df, df_name)
null_data_plot(df, df_name)
# Example usage:
# Assuming 'datasets' is your DataFrame and 'application_train' is one of its components
display_stats(datasets["POS_CASH_balance"], "POS_CASH_balance")
display_feature_info(datasets["POS_CASH_balance"], "POS_CASH_balance")
--------------------------------------------------------------------------------
POS_CASH_balance
--------------------------------------------------------------------------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
# Column Dtype
--- ------ -----
0 SK_ID_PREV int64
1 SK_ID_CURR int64
2 MONTHS_BALANCE int64
3 CNT_INSTALMENT float64
4 CNT_INSTALMENT_FUTURE float64
5 NAME_CONTRACT_STATUS object
6 SK_DPD int64
7 SK_DPD_DEF int64
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
None
---------------------------------------------------------------------------
Shape of the df POS_CASH_balance is (10001358, 8)
---------------------------------------------------------------------------
Statistical summary of POS_CASH_balance is :
---------------------------------------------------------------------------
Description of the df POS_CASH_balance:
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | AMT_GOODS_PRICE | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | OWN_CAR_AGE | FLAG_MOBIL | FLAG_EMP_PHONE | FLAG_WORK_PHONE | FLAG_CONT_MOBILE | FLAG_PHONE | FLAG_EMAIL | CNT_FAM_MEMBERS | REGION_RATING_CLIENT | REGION_RATING_CLIENT_W_CITY | HOUR_APPR_PROCESS_START | REG_REGION_NOT_LIVE_REGION | REG_REGION_NOT_WORK_REGION | LIVE_REGION_NOT_WORK_REGION | REG_CITY_NOT_LIVE_CITY | REG_CITY_NOT_WORK_CITY | LIVE_CITY_NOT_WORK_CITY | EXT_SOURCE_1 | EXT_SOURCE_2 | EXT_SOURCE_3 | APARTMENTS_AVG | BASEMENTAREA_AVG | YEARS_BEGINEXPLUATATION_AVG | YEARS_BUILD_AVG | COMMONAREA_AVG | ELEVATORS_AVG | ENTRANCES_AVG | FLOORSMAX_AVG | FLOORSMIN_AVG | LANDAREA_AVG | LIVINGAPARTMENTS_AVG | LIVINGAREA_AVG | NONLIVINGAPARTMENTS_AVG | NONLIVINGAREA_AVG | APARTMENTS_MODE | BASEMENTAREA_MODE | YEARS_BEGINEXPLUATATION_MODE | YEARS_BUILD_MODE | COMMONAREA_MODE | ELEVATORS_MODE | ENTRANCES_MODE | FLOORSMAX_MODE | FLOORSMIN_MODE | LANDAREA_MODE | LIVINGAPARTMENTS_MODE | LIVINGAREA_MODE | NONLIVINGAPARTMENTS_MODE | NONLIVINGAREA_MODE | APARTMENTS_MEDI | BASEMENTAREA_MEDI | YEARS_BEGINEXPLUATATION_MEDI | YEARS_BUILD_MEDI | COMMONAREA_MEDI | ELEVATORS_MEDI | ENTRANCES_MEDI | FLOORSMAX_MEDI | FLOORSMIN_MEDI | LANDAREA_MEDI | LIVINGAPARTMENTS_MEDI | LIVINGAREA_MEDI | NONLIVINGAPARTMENTS_MEDI | NONLIVINGAREA_MEDI | TOTALAREA_MODE | OBS_30_CNT_SOCIAL_CIRCLE | DEF_30_CNT_SOCIAL_CIRCLE | OBS_60_CNT_SOCIAL_CIRCLE | DEF_60_CNT_SOCIAL_CIRCLE | DAYS_LAST_PHONE_CHANGE | FLAG_DOCUMENT_2 | FLAG_DOCUMENT_3 | FLAG_DOCUMENT_4 | FLAG_DOCUMENT_5 | FLAG_DOCUMENT_6 | FLAG_DOCUMENT_7 | FLAG_DOCUMENT_8 | FLAG_DOCUMENT_9 | FLAG_DOCUMENT_10 | FLAG_DOCUMENT_11 | FLAG_DOCUMENT_12 | FLAG_DOCUMENT_13 | FLAG_DOCUMENT_14 | FLAG_DOCUMENT_15 | FLAG_DOCUMENT_16 | FLAG_DOCUMENT_17 | FLAG_DOCUMENT_18 | FLAG_DOCUMENT_19 | FLAG_DOCUMENT_20 | FLAG_DOCUMENT_21 | AMT_REQ_CREDIT_BUREAU_HOUR | AMT_REQ_CREDIT_BUREAU_DAY | AMT_REQ_CREDIT_BUREAU_WEEK | AMT_REQ_CREDIT_BUREAU_MON | AMT_REQ_CREDIT_BUREAU_QRT | AMT_REQ_CREDIT_BUREAU_YEAR | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 307511.00 | 307511.00 | 307511.00 | 3.075110e+05 | 307511.00 | 307499.00 | 307233.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 104582.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307509.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 134133.00 | 306851.00 | 246546.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 151450.00 | 127568.00 | 157504.00 | 103023.00 | 92646.00 | 143620.00 | 152683.00 | 154491.00 | 98869.00 | 124921.00 | 97312.00 | 153161.00 | 93997.00 | 137829.00 | 159080.00 | 306490.00 | 306490.00 | 306490.00 | 306490.00 | 307510.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.0 | 307511.00 | 307511.0 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 307511.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 | 265992.00 |
| mean | 278180.52 | 0.08 | 0.42 | 1.687979e+05 | 599026.00 | 27108.57 | 538396.21 | 0.02 | -16037.00 | 63815.05 | -4986.12 | -2994.20 | 12.06 | 1.0 | 0.82 | 0.2 | 1.00 | 0.28 | 0.06 | 2.15 | 2.05 | 2.03 | 12.06 | 0.02 | 0.05 | 0.04 | 0.08 | 0.23 | 0.18 | 0.50 | 0.51 | 0.51 | 0.12 | 0.09 | 0.98 | 0.75 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.11 | 0.09 | 0.98 | 0.76 | 0.04 | 0.07 | 0.15 | 0.22 | 0.23 | 0.06 | 0.11 | 0.11 | 0.01 | 0.03 | 0.12 | 0.09 | 0.98 | 0.76 | 0.04 | 0.08 | 0.15 | 0.23 | 0.23 | 0.07 | 0.10 | 0.11 | 0.01 | 0.03 | 0.10 | 1.42 | 0.14 | 1.41 | 0.10 | -962.86 | 0.00 | 0.71 | 0.00 | 0.02 | 0.09 | 0.00 | 0.08 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.01 | 0.01 | 0.03 | 0.27 | 0.27 | 1.90 |
| std | 102790.18 | 0.27 | 0.72 | 2.371231e+05 | 402490.78 | 14493.74 | 369446.46 | 0.01 | 4363.99 | 141275.77 | 3522.89 | 1509.45 | 11.94 | 0.0 | 0.38 | 0.4 | 0.04 | 0.45 | 0.23 | 0.91 | 0.51 | 0.50 | 3.27 | 0.12 | 0.22 | 0.20 | 0.27 | 0.42 | 0.38 | 0.21 | 0.19 | 0.19 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.07 | 0.13 | 0.10 | 0.14 | 0.16 | 0.08 | 0.10 | 0.11 | 0.05 | 0.07 | 0.11 | 0.08 | 0.06 | 0.11 | 0.08 | 0.13 | 0.10 | 0.15 | 0.16 | 0.08 | 0.09 | 0.11 | 0.05 | 0.07 | 0.11 | 2.40 | 0.45 | 2.38 | 0.36 | 826.81 | 0.01 | 0.45 | 0.01 | 0.12 | 0.28 | 0.01 | 0.27 | 0.06 | 0.0 | 0.06 | 0.0 | 0.06 | 0.05 | 0.03 | 0.10 | 0.02 | 0.09 | 0.02 | 0.02 | 0.02 | 0.08 | 0.11 | 0.20 | 0.92 | 0.79 | 1.87 |
| min | 100002.00 | 0.00 | 0.00 | 2.565000e+04 | 45000.00 | 1615.50 | 40500.00 | 0.00 | -25229.00 | -17912.00 | -24672.00 | -7197.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | -4292.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 25% | 189145.50 | 0.00 | 0.00 | 1.125000e+05 | 270000.00 | 16524.00 | 238500.00 | 0.01 | -19682.00 | -2760.00 | -7479.50 | -4299.00 | 5.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 10.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.33 | 0.39 | 0.37 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.05 | 0.04 | 0.98 | 0.70 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.04 | 0.00 | 0.00 | 0.06 | 0.04 | 0.98 | 0.69 | 0.01 | 0.00 | 0.07 | 0.17 | 0.08 | 0.02 | 0.05 | 0.05 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | -1570.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 50% | 278202.00 | 0.00 | 0.00 | 1.471500e+05 | 513531.00 | 24903.00 | 450000.00 | 0.02 | -15750.00 | -1213.00 | -4504.00 | -3254.00 | 9.00 | 1.0 | 1.00 | 0.0 | 1.00 | 0.00 | 0.00 | 2.00 | 2.00 | 2.00 | 12.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.51 | 0.57 | 0.54 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.08 | 0.07 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.09 | 0.08 | 0.98 | 0.76 | 0.02 | 0.00 | 0.14 | 0.17 | 0.21 | 0.05 | 0.08 | 0.07 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | -757.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 |
| 75% | 367142.50 | 0.00 | 1.00 | 2.025000e+05 | 808650.00 | 34596.00 | 679500.00 | 0.03 | -12413.00 | -289.00 | -2010.00 | -1720.00 | 15.00 | 1.0 | 1.00 | 0.0 | 1.00 | 1.00 | 0.00 | 3.00 | 2.00 | 2.00 | 14.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.68 | 0.66 | 0.67 | 0.15 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.14 | 0.11 | 0.99 | 0.82 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.08 | 0.13 | 0.13 | 0.00 | 0.02 | 0.15 | 0.11 | 0.99 | 0.83 | 0.05 | 0.12 | 0.21 | 0.33 | 0.38 | 0.09 | 0.12 | 0.13 | 0.00 | 0.03 | 0.13 | 2.00 | 0.00 | 2.00 | 0.00 | -274.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.0 | 0.00 | 0.0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.00 |
| max | 456255.00 | 1.00 | 19.00 | 1.170000e+08 | 4050000.00 | 258025.50 | 4050000.00 | 0.07 | -7489.00 | 365243.00 | 0.00 | 0.00 | 91.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 20.00 | 3.00 | 3.00 | 23.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 348.00 | 34.00 | 344.00 | 24.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.0 | 1.00 | 1.0 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 4.00 | 9.00 | 8.00 | 27.00 | 261.00 | 25.00 |
None
Description of the df continued for POS_CASH_balance:
---------------------------------------------------------------------------
Data type value counts:
int64 5
float64 2
object 1
Name: count, dtype: int64
Return the number of unique elements in the object.
NAME_CONTRACT_STATUS 9
dtype: int64
---------------------------------------------------------------------------
Categorical and Numerical(int + float) features of POS_CASH_balance.
---------------------------------------------------------------------------
{'int64': Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'SK_DPD', 'SK_DPD_DEF'], dtype='object')}
------------------------------
{'float64': Index(['CNT_INSTALMENT', 'CNT_INSTALMENT_FUTURE'], dtype='object')}
------------------------------
{'object': Index(['NAME_CONTRACT_STATUS'], dtype='object')}
------------------------------
---------------------------------------------------------------------------
---------------------------------------------------------------------------
The Missing Data:
| Percent | Train Missing Count | |
|---|---|---|
| CNT_INSTALMENT_FUTURE | 0.26 | 26087 |
| CNT_INSTALMENT | 0.26 | 26071 |
---------------------------------------------------------------------------
POS = datasets["POS_CASH_balance"]
POS.shape
(10001358, 8)
POS.columns
Index(['SK_ID_PREV', 'SK_ID_CURR', 'MONTHS_BALANCE', 'CNT_INSTALMENT',
'CNT_INSTALMENT_FUTURE', 'NAME_CONTRACT_STATUS', 'SK_DPD',
'SK_DPD_DEF'],
dtype='object')
POS.describe()
| SK_ID_PREV | SK_ID_CURR | MONTHS_BALANCE | CNT_INSTALMENT | CNT_INSTALMENT_FUTURE | SK_DPD | SK_DPD_DEF | |
|---|---|---|---|---|---|---|---|
| count | 1.000136e+07 | 1.000136e+07 | 1.000136e+07 | 9.975287e+06 | 9.975271e+06 | 1.000136e+07 | 1.000136e+07 |
| mean | 1.903217e+06 | 2.784039e+05 | -3.501259e+01 | 1.708965e+01 | 1.048384e+01 | 1.160693e+01 | 6.544684e-01 |
| std | 5.358465e+05 | 1.027637e+05 | 2.606657e+01 | 1.199506e+01 | 1.110906e+01 | 1.327140e+02 | 3.276249e+01 |
| min | 1.000001e+06 | 1.000010e+05 | -9.600000e+01 | 1.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 25% | 1.434405e+06 | 1.895500e+05 | -5.400000e+01 | 1.000000e+01 | 3.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 50% | 1.896565e+06 | 2.786540e+05 | -2.800000e+01 | 1.200000e+01 | 7.000000e+00 | 0.000000e+00 | 0.000000e+00 |
| 75% | 2.368963e+06 | 3.674290e+05 | -1.300000e+01 | 2.400000e+01 | 1.400000e+01 | 0.000000e+00 | 0.000000e+00 |
| max | 2.843499e+06 | 4.562550e+05 | -1.000000e+00 | 9.200000e+01 | 8.500000e+01 | 4.231000e+03 | 3.595000e+03 |
POS.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10001358 entries, 0 to 10001357 Data columns (total 8 columns): # Column Dtype --- ------ ----- 0 SK_ID_PREV int64 1 SK_ID_CURR int64 2 MONTHS_BALANCE int64 3 CNT_INSTALMENT float64 4 CNT_INSTALMENT_FUTURE float64 5 NAME_CONTRACT_STATUS object 6 SK_DPD int64 7 SK_DPD_DEF int64 dtypes: float64(2), int64(5), object(1) memory usage: 610.4+ MB
plt.figure(figsize=(10, 6))
sns.histplot(data=POS, x='MONTHS_BALANCE', bins=10, kde=True, color='skyblue', edgecolor='black')
plt.title('Histogram of Months Balance')
plt.xlabel('Months Balance')
plt.ylabel('Count')
plt.show()
#Testing Contract Status Variable
plt.figure(figsize=(16,8))
sns.set_theme()
sns.countplot(x = 'NAME_CONTRACT_STATUS',data = POS)
plt.xlabel("Contract Status",fontweight='bold',size=13)
plt.ylabel("Number",fontweight='bold',size=13)
plt.show()
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming 'POS' is your DataFrame
# If not already loaded, you can load it using: POS = pd.read_csv('your_dataset.csv')
# Select only numeric columns
numeric_columns = POS.select_dtypes(include='number').columns
# Exclude variables from the list
exclude_variables = ['SK_ID_PREV', 'SK_ID_CURR']
selected_variables = [col for col in numeric_columns if col not in exclude_variables]
# Create a DataFrame with selected variables
correlation_data = POS[selected_variables]
# Calculate the correlation matrix
correlation_matrix = correlation_data.corr()
# Plot the correlation matrix heatmap
plt.figure(figsize=(16, 14))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=1)
plt.title('Correlation Matrix for Installment Payments')
plt.show()
Defaulters among the highly categorical features are seen in most, highlighting Organization Type, Family Type , Occupation Type & Education.
Noticeable correlations:
Amount of credit and amount of goods price show a strong correlation. Days of birth and days employed exhibit a strong correlation. There is a strong correlation between external source 1 and days of birth. These observations suggest potential opportunities for feature engineering.
list(datasets.keys())
['application_train', 'application_test', 'bureau', 'bureau_balance', 'credit_card_balance', 'installments_payments', 'previous_application', 'POS_CASH_balance']
len(datasets["application_train"]["SK_ID_CURR"].unique()) == datasets["application_train"].shape[0]
True
# is there an overlap between the test and train customers
np.intersect1d(datasets["application_train"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])
array([], dtype=int64)
#
datasets["application_test"].shape
(48744, 121)
datasets["application_train"].shape
(307511, 122)
The persons in the kaggle submission file have had previous applications in the previous_application.csv. 47,800 out 48,744 people have had previous appications.
appsDF = datasets["previous_application"]
display(appsDF.head())
print(f"{appsDF.shape[0]:,} rows, {appsDF.shape[1]:,} columns")
| SK_ID_PREV | SK_ID_CURR | NAME_CONTRACT_TYPE | AMT_ANNUITY | AMT_APPLICATION | AMT_CREDIT | AMT_DOWN_PAYMENT | AMT_GOODS_PRICE | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | ... | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2030495 | 271877 | Consumer loans | 1730.430 | 17145.0 | 17145.0 | 0.0 | 17145.0 | SATURDAY | 15 | ... | Connectivity | 12.0 | middle | POS mobile with interest | 365243.0 | -42.0 | 300.0 | -42.0 | -37.0 | 0.0 |
| 1 | 2802425 | 108129 | Cash loans | 25188.615 | 607500.0 | 679671.0 | NaN | 607500.0 | THURSDAY | 11 | ... | XNA | 36.0 | low_action | Cash X-Sell: low | 365243.0 | -134.0 | 916.0 | 365243.0 | 365243.0 | 1.0 |
| 2 | 2523466 | 122040 | Cash loans | 15060.735 | 112500.0 | 136444.5 | NaN | 112500.0 | TUESDAY | 11 | ... | XNA | 12.0 | high | Cash X-Sell: high | 365243.0 | -271.0 | 59.0 | 365243.0 | 365243.0 | 1.0 |
| 3 | 2819243 | 176158 | Cash loans | 47041.335 | 450000.0 | 470790.0 | NaN | 450000.0 | MONDAY | 7 | ... | XNA | 12.0 | middle | Cash X-Sell: middle | 365243.0 | -482.0 | -152.0 | -182.0 | -177.0 | 1.0 |
| 4 | 1784265 | 202054 | Cash loans | 31924.395 | 337500.0 | 404055.0 | NaN | 337500.0 | THURSDAY | 9 | ... | XNA | 24.0 | high | Cash Street: high | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 37 columns
1,670,214 rows, 37 columns
print(f"There are {appsDF.shape[0]:,} previous applications")
There are 1,670,214 previous applications
#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_train"]["SK_ID_CURR"])):,}')
Number of train applicants with previous applications is 291,057
#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])):,}')
Number of train applicants with previous applications is 47,800
# How many previous applciations per applicant in the previous_application
prevAppCounts = appsDF['SK_ID_CURR'].value_counts(dropna=False)
len(prevAppCounts[prevAppCounts >40]) #more that 40 previous applications
plt.hist(prevAppCounts[prevAppCounts>=0], bins=100)
plt.grid()
prevAppCounts[prevAppCounts >50].plot(kind='bar')
plt.xticks(rotation=25)
plt.show()
sum(appsDF['SK_ID_CURR'].value_counts()==1)
60458
plt.hist(appsDF['SK_ID_CURR'].value_counts(), cumulative =True, bins = 100);
plt.grid()
plt.ylabel('cumulative number of IDs')
plt.xlabel('Number of previous applications per ID')
plt.title('Histogram of Number of previous applications for an ID')
Text(0.5, 1.0, 'Histogram of Number of previous applications for an ID')
* Low = <5 claims (22%)
* Medium = 10 to 39 claims (58%)
* High = 40 or more claims (20%)
apps_all = appsDF['SK_ID_CURR'].nunique()
apps_5plus = appsDF['SK_ID_CURR'].value_counts()>=5
apps_40plus = appsDF['SK_ID_CURR'].value_counts()>=40
print('Percentage with 10 or more previous apps:', np.round(100.*(sum(apps_5plus)/apps_all),5))
print('Percentage with 40 or more previous apps:', np.round(100.*(sum(apps_40plus)/apps_all),5))
Percentage with 10 or more previous apps: 41.76895 Percentage with 40 or more previous apps: 0.03453
In the case of the HCDR competition (and many other machine learning problems that involve multiple tables in 3NF or not) we need to join these datasets (denormalize) when using a machine learning pipeline. Joining the secondary tables with the primary table will lead to lots of new features about each loan application; these features will tend to be aggregate type features or meta data about the loan or its application. How can we do this when using Machine Learning Pipelines?
previous_application with application_x¶We refer to the application_train data (and also application_test data also) as the primary table and the other files as the secondary tables (e.g., previous_application dataset). All tables can be joined using the primary key SK_ID_PREV.
Let's assume we wish to generate a feature based on previous application attempts. In this case, possible features here could be:
AMT_APPLICATION, AMT_CREDIT could be based on average, min, max, median, etc.To build such features, we need to join the application_train data (and also application_test data also) with the 'previous_application' dataset (and the other available datasets).
When joining this data in the context of pipelines, different strategies come to mind with various tradeoffs:
application_train data (the labeled dataset) and with the application_test data (the unlabeled submission dataset) prior to processing the data (in a train, valid, test partition) via your machine learning pipeline. [This approach is recommended for this HCDR competition. WHY?]I want you to think about this section and build on this.
application_train data (the labeled dataset) and with the application_test data (the unlabeled submission dataset)), thereby leading to X_train, y_train, X_valid, etc.appsDF.columns
Index(['SK_ID_PREV', 'SK_ID_CURR', 'NAME_CONTRACT_TYPE', 'AMT_ANNUITY',
'AMT_APPLICATION', 'AMT_CREDIT', 'AMT_DOWN_PAYMENT', 'AMT_GOODS_PRICE',
'WEEKDAY_APPR_PROCESS_START', 'HOUR_APPR_PROCESS_START',
'FLAG_LAST_APPL_PER_CONTRACT', 'NFLAG_LAST_APPL_IN_DAY',
'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
'RATE_INTEREST_PRIVILEGED', 'NAME_CASH_LOAN_PURPOSE',
'NAME_CONTRACT_STATUS', 'DAYS_DECISION', 'NAME_PAYMENT_TYPE',
'CODE_REJECT_REASON', 'NAME_TYPE_SUITE', 'NAME_CLIENT_TYPE',
'NAME_GOODS_CATEGORY', 'NAME_PORTFOLIO', 'NAME_PRODUCT_TYPE',
'CHANNEL_TYPE', 'SELLERPLACE_AREA', 'NAME_SELLER_INDUSTRY',
'CNT_PAYMENT', 'NAME_YIELD_GROUP', 'PRODUCT_COMBINATION',
'DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION',
'DAYS_LAST_DUE', 'DAYS_TERMINATION', 'NFLAG_INSURED_ON_APPROVAL'],
dtype='object')
appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]
| SK_ID_PREV | SK_ID_CURR | NAME_CONTRACT_TYPE | AMT_ANNUITY | AMT_APPLICATION | AMT_CREDIT | AMT_DOWN_PAYMENT | AMT_GOODS_PRICE | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | ... | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 2315218 | 175704 | Cash loans | NaN | 0.0 | 0.0 | NaN | NaN | TUESDAY | 11 | ... | XNA | NaN | XNA | Cash | NaN | NaN | NaN | NaN | NaN | NaN |
1 rows × 37 columns
appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]["AMT_CREDIT"]
6 0.0 Name: AMT_CREDIT, dtype: float64
appsDF[0:50][(appsDF["SK_ID_CURR"]==175704) & ~(appsDF["AMT_CREDIT"]==1.0)]
| SK_ID_PREV | SK_ID_CURR | NAME_CONTRACT_TYPE | AMT_ANNUITY | AMT_APPLICATION | AMT_CREDIT | AMT_DOWN_PAYMENT | AMT_GOODS_PRICE | WEEKDAY_APPR_PROCESS_START | HOUR_APPR_PROCESS_START | ... | NAME_SELLER_INDUSTRY | CNT_PAYMENT | NAME_YIELD_GROUP | PRODUCT_COMBINATION | DAYS_FIRST_DRAWING | DAYS_FIRST_DUE | DAYS_LAST_DUE_1ST_VERSION | DAYS_LAST_DUE | DAYS_TERMINATION | NFLAG_INSURED_ON_APPROVAL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 2315218 | 175704 | Cash loans | NaN | 0.0 | 0.0 | NaN | NaN | TUESDAY | 11 | ... | XNA | NaN | XNA | Cash | NaN | NaN | NaN | NaN | NaN | NaN |
1 rows × 37 columns
appsDF.isna().sum()
SK_ID_PREV 0 SK_ID_CURR 0 NAME_CONTRACT_TYPE 0 AMT_ANNUITY 372235 AMT_APPLICATION 0 AMT_CREDIT 1 AMT_DOWN_PAYMENT 895844 AMT_GOODS_PRICE 385515 WEEKDAY_APPR_PROCESS_START 0 HOUR_APPR_PROCESS_START 0 FLAG_LAST_APPL_PER_CONTRACT 0 NFLAG_LAST_APPL_IN_DAY 0 RATE_DOWN_PAYMENT 895844 RATE_INTEREST_PRIMARY 1664263 RATE_INTEREST_PRIVILEGED 1664263 NAME_CASH_LOAN_PURPOSE 0 NAME_CONTRACT_STATUS 0 DAYS_DECISION 0 NAME_PAYMENT_TYPE 0 CODE_REJECT_REASON 0 NAME_TYPE_SUITE 820405 NAME_CLIENT_TYPE 0 NAME_GOODS_CATEGORY 0 NAME_PORTFOLIO 0 NAME_PRODUCT_TYPE 0 CHANNEL_TYPE 0 SELLERPLACE_AREA 0 NAME_SELLER_INDUSTRY 0 CNT_PAYMENT 372230 NAME_YIELD_GROUP 0 PRODUCT_COMBINATION 346 DAYS_FIRST_DRAWING 673065 DAYS_FIRST_DUE 673065 DAYS_LAST_DUE_1ST_VERSION 673065 DAYS_LAST_DUE 673065 DAYS_TERMINATION 673065 NFLAG_INSURED_ON_APPROVAL 673065 dtype: int64
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
from time import time, ctime
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.utils import resample
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, log_loss, classification_report, roc_auc_score, make_scorer
from scipy import stats
import json
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import make_pipeline, Pipeline, FeatureUnion
from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder
class FeaturesAggregator(BaseEstimator, TransformerMixin):
def __init__(self, file_name, features=None): # no *args or **kargs
self.features = features
self.agg_op_features = {}
for f in self.features:
temp = {f"{file_name}_{f}_{func}":func for func in ['min', 'max', 'mean', 'count', 'sum']}
self.agg_op_features[f]=[(k, v) for k, v in temp.items()]
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
group_cols = ["SK_ID_CURR"]
result = X.groupby(group_cols).agg(self.agg_op_features)
result.columns = result.columns.droplevel()
result = result.reset_index(level=["SK_ID_CURR"])
return result # return dataframe with the join key "SK_ID_CURR"
class EngineerFeatures(BaseEstimator, TransformerMixin):
def __init__(self, features=None):
self
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
# Flag to represent when Total income is greater than Credit
X['INCOME_GT_CREDIT_FLAG'] = X['AMT_INCOME_TOTAL'] > X['AMT_CREDIT']
# Column to represent Credit Income Percent
X['CREDIT_INCOME_PERCENT'] = X['AMT_CREDIT'] / X['AMT_INCOME_TOTAL']
# Column to represent Annuity Income percent
X['ANNUITY_INCOME_PERCENT'] = X['AMT_ANNUITY'] / X['AMT_INCOME_TOTAL']
# Column to represent Credit Term
X['CREDIT_TERM'] = X['AMT_CREDIT'] / X['AMT_ANNUITY']
# Column to represent Days Employed percent in his life
X['DAYS_EMPLOYED_PERCENT'] = X['DAYS_EMPLOYED'] / X['DAYS_BIRTH']
return X
from sklearn.pipeline import make_pipeline, Pipeline, FeatureUnion
prevApps_features = ['AMT_ANNUITY', 'AMT_APPLICATION']
bureau_features = ['AMT_ANNUITY', 'AMT_CREDIT_SUM']
bureau_bal_features = ['MONTHS_BALANCE']
cc_bal_features = ['MONTHS_BALANCE', 'AMT_BALANCE', 'CNT_INSTALMENT_MATURE_CUM']
installments_pmnts_features = ['AMT_INSTALMENT', 'AMT_PAYMENT']
appsTrainDF = datasets['application_train']
engineer_features = EngineerFeatures()
appsTrainDF = engineer_features.transform(appsTrainDF)
prevAppsDF = datasets["previous_application"]
features_aggregator = FeaturesAggregator('prevApps', features=prevApps_features)
prevApps_aggregated = features_aggregator.transform(prevAppsDF)
bureauDF = datasets["bureau"]
features_aggregator = FeaturesAggregator('bureau', features=bureau_features)
bureau_aggregated = features_aggregator.transform(bureauDF)
#bureaubalDF = datasets['bureau_balance']
#features_aggregator = FeaturesAggregator(features=bureau_bal_features)
#prevApps_aggregated = features_aggregator.transform(bureaubalDF)
ccbalDF = datasets["credit_card_balance"]
features_aggregator = FeaturesAggregator('credit_card_balance', features=cc_bal_features)
ccblance_aggregated = features_aggregator.transform(ccbalDF)
installmentspaymentsDF = datasets["installments_payments"]
features_aggregator = FeaturesAggregator('credit_card_balance', features=installments_pmnts_features)
installments_pmnts_aggregated = features_aggregator.transform(installmentspaymentsDF)
merge_all_data = True
# merge primary table and secondary tables using features based on meta data and aggregage stats
if merge_all_data:
appsTrainDF = appsTrainDF.merge(prevApps_aggregated, how='left', on='SK_ID_CURR')
appsTrainDF = appsTrainDF.merge(bureau_aggregated, how='left', on="SK_ID_CURR")
appsTrainDF = appsTrainDF.merge(ccblance_aggregated, how='left', on="SK_ID_CURR")
appsTrainDF = appsTrainDF.merge(installments_pmnts_aggregated, how='left', on="SK_ID_CURR")
appsTrainDF.shape
(307511, 172)
def plot_confusion_matrix(test_y, predicted_y):
# Confusion matrix
C = confusion_matrix(test_y, predicted_y)
# Recall matrix
A = (((C.T)/(C.sum(axis=1))).T)
# Precision matrix
B = (C/C.sum(axis=0))
plt.figure(figsize=(20,4))
labels = ['Re-paid(0)','Not Re-paid(1)']
cmap=sns.light_palette("purple")
plt.subplot(1,3,1)
sns.heatmap(C, annot=True, cmap=cmap,fmt="d", xticklabels = labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Orignal Class')
plt.title('Confusion matrix')
plt.subplot(1,3,2)
sns.heatmap(A, annot=True, cmap=cmap, xticklabels = labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Orignal Class')
plt.title('Recall matrix')
plt.subplot(1,3,3)
sns.heatmap(B, annot=True, cmap=cmap, xticklabels = labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Orignal Class')
plt.title('Precision matrix')
plt.show()
try:
expLog
except NameError:
expLog = pd.DataFrame(columns=["exp_name",
"Train Acc",
"Valid Acc",
"Test Acc",
"Train AUC",
"Valid AUC",
"Test AUC",
"Train F1 Score",
"Test F1 Score",
"Train Log Loss",
"Test Log Loss",
"P Score",
"Train Time",
"Test Time",
"Description"
])
def pct(x):
return round(100*x, 3)
num_attribs = [
'AMT_INCOME_TOTAL',
'AMT_CREDIT',
'EXT_SOURCE_3',
'EXT_SOURCE_2',
'EXT_SOURCE_1',
'DAYS_EMPLOYED',
'DAYS_BIRTH',
'FLOORSMAX_AVG',
'FLOORSMAX_MEDI',
'FLOORSMAX_MODE',
'AMT_GOODS_PRICE',
'REGION_POPULATION_RELATIVE',
'ELEVATORS_AVG',
'REG_CITY_NOT_LIVE_CITY',
'FLAG_EMP_PHONE',
'REG_CITY_NOT_WORK_CITY',
'DAYS_ID_PUBLISH',
'DAYS_LAST_PHONE_CHANGE',
'REGION_RATING_CLIENT',
'REGION_RATING_CLIENT_W_CITY',
## Highly correlated previous applications
'prevApps_AMT_ANNUITY_mean',
## Highly correlated Credit card balance features
'credit_card_balance_MONTHS_BALANCE_count',
'credit_card_balance_AMT_BALANCE_count',
'credit_card_balance_CNT_INSTALMENT_MATURE_CUM_count',
'credit_card_balance_CNT_INSTALMENT_MATURE_CUM_sum',
'credit_card_balance_MONTHS_BALANCE_sum',
'credit_card_balance_MONTHS_BALANCE_min',
'credit_card_balance_MONTHS_BALANCE_mean',
'credit_card_balance_AMT_BALANCE_min',
'credit_card_balance_AMT_BALANCE_max',
'credit_card_balance_AMT_BALANCE_mean'
]
cat_attribs = ['CODE_GENDER', 'FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE','NAME_EDUCATION_TYPE','OCCUPATION_TYPE','NAME_INCOME_TYPE']
selected_features = num_attribs + cat_attribs
tot_features = f"{len(selected_features)}: Num:{len(num_attribs)}, Cat:{len(cat_attribs)}"
#Total Feature selected for processing
tot_features
'38: Num:31, Cat:7'
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('imputer', SimpleImputer(strategy='most_frequent')),
('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
])
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_attribs)),
('imputer', SimpleImputer(strategy='mean')),
('std_scaler', StandardScaler()),
])
data_prep_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
# Split Sample to feed the pipeline and it will result in a new dataset that is (1 / splits) the size
splits = 3
# Train Test split percentage
subsample_rate = 0.3
train_dataset = appsTrainDF
finaldf = np.array_split(train_dataset, splits)
X_train = finaldf[0][selected_features]
y_train = finaldf[0]['TARGET']
## split part of data
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, stratify=y_train, test_size=subsample_rate, random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, stratify=y_train, test_size=0.15, random_state=42)
print(f"X train shape: {X_train.shape}")
print(f"X validation shape: {X_valid.shape}")
print(f"X test shape: {X_test.shape}")
X train shape: (60989, 38) X validation shape: (10763, 38) X test shape: (30752, 38)
cvSplits = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0)
X_train.head(5)
| AMT_INCOME_TOTAL | AMT_CREDIT | EXT_SOURCE_3 | EXT_SOURCE_2 | EXT_SOURCE_1 | DAYS_EMPLOYED | DAYS_BIRTH | FLOORSMAX_AVG | FLOORSMAX_MEDI | FLOORSMAX_MODE | ... | credit_card_balance_AMT_BALANCE_min | credit_card_balance_AMT_BALANCE_max | credit_card_balance_AMT_BALANCE_mean | CODE_GENDER | FLAG_OWN_REALTY | FLAG_OWN_CAR | NAME_CONTRACT_TYPE | NAME_EDUCATION_TYPE | OCCUPATION_TYPE | NAME_INCOME_TYPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40832 | 117000.0 | 157500.0 | 0.729567 | 0.262060 | 0.505998 | -439.0 | -16633.0 | 0.1667 | 0.1667 | 0.1667 | ... | NaN | NaN | NaN | F | 1 | 0 | 0 | Secondary / secondary special | Sales staff | Working |
| 36820 | 166500.0 | 900000.0 | 0.743559 | 0.451283 | 0.600909 | 365243.0 | -22564.0 | 0.1667 | 0.1667 | 0.1667 | ... | 0.0 | 194627.34 | 40994.615602 | F | 1 | 1 | 0 | Secondary / secondary special | Laborers | Pensioner |
| 81804 | 90000.0 | 495000.0 | 0.535276 | 0.480293 | 0.505998 | -434.0 | -15989.0 | 0.1667 | 0.1667 | 0.1667 | ... | 0.0 | 0.00 | 0.000000 | M | 0 | 0 | 0 | Secondary / secondary special | Laborers | Working |
| 35092 | 112500.0 | 508495.5 | 0.722393 | 0.260275 | 0.505998 | 365243.0 | -22918.0 | 0.1667 | 0.1667 | 0.1667 | ... | NaN | NaN | NaN | F | 1 | 1 | 0 | Lower secondary | Laborers | Pensioner |
| 57197 | 135000.0 | 400500.0 | 0.304672 | 0.526361 | 0.275414 | -641.0 | -12513.0 | 0.1667 | 0.1667 | 0.1667 | ... | 0.0 | 64450.80 | 3229.703053 | M | 1 | 0 | 0 | Secondary / secondary special | Core staff | Working |
5 rows × 38 columns
pipeline = Pipeline([
("prep", data_prep_pipeline),
("clf", LogisticRegression(solver='saga',random_state=42))
])
start = time()
model = pipeline.fit(X_train, y_train)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(pipeline, X_train , y_train, cv=cvSplits)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = pipeline.score(X_test, y_test)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_{len(selected_features)}_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid, model.predict(X_valid))),
pct(accuracy_score(y_test, model.predict(X_test))),
roc_auc_score(y_train, model.predict_proba(X_train)[:, 1]),
roc_auc_score(y_valid, model.predict_proba(X_valid)[:, 1]),
roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]),
f1_score(y_train, model.predict(X_train)),
f1_score(y_test, model.predict(X_test)),
log_loss(y_train, model.predict(X_train)),
log_loss(y_test, model.predict(X_test)),0 ],4)) \
+ [train_time,test_time] + [f"Imbalanced Logistic reg with 20% training data"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.88 | 91.815 | 0.7412 | 0.7362 | 0.737 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test,model.predict_proba(X_test)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true = y_test
y_pred_proba = model.predict_proba(X_test)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred = (y_pred_proba > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
Since the dataset is imbalanced in the favor of majority samples being where the loans are repaid (TARGET=1) in a ratio more than 10:1, we can resample the data, by undersampling the majority class to make the data more balanced. So that we do not lose too much valuable data, the number of majority class samples is kept as twice the number of minority class.
# Down-sample Majority Class
train = pd.concat([X_train, y_train], axis=1)
count = train['TARGET'].value_counts()
num_majority = count[0]
num_minority = count[1]
#Number of undersampled majority class 2 x minority class
num_undersample_majority = 2 * num_minority
#separating majority and minority classes
df_majority = train[train["TARGET"] == 0]
df_minority = train[train["TARGET"] == 1]
df_majority_undersampled = resample(df_majority, replace=False, n_samples=num_undersample_majority, random_state=42)
df_undersampled = pd.concat([df_minority, df_majority_undersampled], axis=0)
#splitting dependent and independent variables
X_train = df_undersampled[selected_features]
y_train = df_undersampled['TARGET']
df_undersampled.TARGET.value_counts()
TARGET 0.0 9902 1.0 4951 Name: count, dtype: int64
cvSplits = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0)
pipeline = Pipeline([
("prep", data_prep_pipeline),
("clf", LogisticRegression(solver='saga',random_state=42))
])
start = time()
model = pipeline.fit(X_train, y_train)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(pipeline, X_train , y_train, cv=cvSplits)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = pipeline.score(X_test, y_test)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_{len(selected_features)}_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid, model.predict(X_valid))),
pct(accuracy_score(y_test, model.predict(X_test))),
roc_auc_score(y_train, model.predict_proba(X_train)[:, 1]),
roc_auc_score(y_valid, model.predict_proba(X_valid)[:, 1]),
roc_auc_score(y_test, model.predict_proba(X_test)[:, 1]),
f1_score(y_train, model.predict(X_train)),
f1_score(y_test, model.predict(X_test)),
log_loss(y_train, model.predict(X_train)),
log_loss(y_test, model.predict(X_test)),0 ],4)) \
+ [train_time,test_time] + [f"Balanced Logistic reg with 30% training data"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test,model.predict_proba(X_test)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true = y_test
y_pred_proba = model.predict_proba(X_test)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred = (y_pred_proba > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
Benefits of Feature Engineering
An effective Feature Engineering implies:
Our feature engineering endeavors encompassed several key aspects, delineated as follows:
Incorporating Domain-Specific Insights: The integration of custom domain knowledge played a pivotal role in the formulation of unique features tailored to our dataset.
Crafting Engineered Aggregated Features: A deliberate effort was made to create novel aggregated features through meticulous engineering, enhancing the dataset's overall representational capacity.
Exploratory Modeling of the Data: We delved into experimental modeling techniques, aiming to uncover hidden patterns and relationships within the dataset that might have eluded conventional analysis.
Validation of Manual One-Hot Encoding (OHE): Rigorous validation processes were applied to ensure the accuracy and effectiveness of manually applied One-Hot Encoding, a critical step in categorical data representation.
Polynomial Feature Expansion (Degree 4): A sophisticated approach involved the generation of polynomial features up to the fourth degree for select variables, amplifying the complexity and richness of the feature set.
Comprehensive Dataset Merging: All relevant datasets were systematically merged, fostering a holistic view of the data and promoting comprehensive analyses.
Pruning Columns with Missing Values: To enhance the dataset's integrity, columns with missing values were judiciously identified and subsequently removed, streamlining the dataset for further analysis.
A pivotal step in the feature engineering process involves the integration of domain knowledge-based features, a critical factor in enhancing model accuracy. Initially, we undertook the task of identifying these features for each dataset. Among the novel custom features introduced were metrics such as post-payment credit card balance relative to the due amount, average application amount, credit average, available credit as a percentage of income, annuity as a percentage of income, and annuity as a percentage of available credit.
Subsequently, we delved into numerical feature identification and aggregation, employing mean, minimum, and maximum values. Although an attempt was made to implement label encoding for unique values exceeding 5 during the engineering phase, a strategic decision led to the application of One-Hot Encoding (OHE) at the pipeline level. This targeted specific highly correlated fields in the final merged dataset, optimizing code management.
Extensive feature engineering was executed through multiple modeling approaches, involving primary, secondary, and tertiary tables, culminating in an optimized approach with minimal memory usage. The first attempt focused on creating engineered and aggregated features for Key-Level 3 tables, merging them with Key-Level 2 tables, and ultimately combining them with the primary dataset. However, this approach resulted in a surplus of redundant features, consuming significant memory.
In Attempt 2, a streamlined approach was adopted, creating custom and aggregated features for Key-Level 3 tables, merging them with Key-Level 2 tables based on the primary key, and extending this to Key-Level 1 tables using additional aggregated columns. This approach reduced duplicates, optimized memory usage, and employed a garbage collector after each merge.
In Attempt 3, the merged dataframe from the previous attempt was further enriched with polynomial features of degree 4. A final merge of Key-Level 3, Key-Level 2, and Key-Level 1 datasets formed the training dataframe, with meticulous attention to ensuring that no columns had more than 50% missing data.
The process of engineering and incorporating these features into the model, coupled with judicious splits during testing, initially yielded lower accuracy. However, deploying these merged features with well-considered splits during the training phase resulted in improved accuracy and diminished risk of overfitting, especially notable in models like Random Forest and XGBoost.
Future endeavors include implementing label encoding for all unique categorical values, exploring techniques such as PCA or custom functions to address multicollinearity in the pipeline, eliminating low-importance features, and evaluating their impact on model accuracy.
data_app_train = freshdata['application_train']
data_app_test = freshdata['application_test']
# Function to calculate missing values by column
def missing_values(df):
# Total missing values
mis_val = df.isnull().sum()
# Percentage of missing values
mis_val_percent = 100 * df.isnull().sum() / len(df)
# Make a table with the results
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
# Rename the columns
mis_val_table_ren_columns = mis_val_table.rename(
columns = {0 : 'Missing Values', 1 : '% of Total Values'})
# Sort the table by percentage of missing descending
mis_val_table_ren_columns = mis_val_table_ren_columns[
mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
'% of Total Values', ascending=False).round(1)
# Print some summary information
print ("The dataframe has " + str(df.shape[1]) + " columns.\n"
"There are " + str(mis_val_table_ren_columns.shape[0]) +
" columns that have missing values.")
return mis_val_table_ren_columns
missing_values(data_app_train)
The dataframe has 122 columns. There are 67 columns that have missing values.
| Missing Values | % of Total Values | |
|---|---|---|
| COMMONAREA_MEDI | 214865 | 69.9 |
| COMMONAREA_AVG | 214865 | 69.9 |
| COMMONAREA_MODE | 214865 | 69.9 |
| NONLIVINGAPARTMENTS_MEDI | 213514 | 69.4 |
| NONLIVINGAPARTMENTS_MODE | 213514 | 69.4 |
| ... | ... | ... |
| EXT_SOURCE_2 | 660 | 0.2 |
| AMT_GOODS_PRICE | 278 | 0.1 |
| AMT_ANNUITY | 12 | 0.0 |
| CNT_FAM_MEMBERS | 2 | 0.0 |
| DAYS_LAST_PHONE_CHANGE | 1 | 0.0 |
67 rows × 2 columns
data_app_train_num = data_app_train.select_dtypes(include=[np.number]).drop('SK_ID_CURR', axis = 1)
LowerOut = data_app_train_num.quantile(0.025)
HigherOut = data_app_train_num.quantile(0.975)
Outliers = (data_app_train_num < LowerOut) | (data_app_train_num > HigherOut)
print(Outliers.sum().sort_values())
TARGET 0
FLAG_DOCUMENT_3 0
FLAG_DOCUMENT_6 0
FLAG_DOCUMENT_8 0
REG_CITY_NOT_WORK_CITY 0
...
DAYS_ID_PUBLISH 15338
EXT_SOURCE_2 15344
DAYS_REGISTRATION 15360
AMT_ANNUITY 15364
DAYS_BIRTH 15366
Length: 105, dtype: int64
from sklearn.impute import SimpleImputer
# select numerical columns
numerical_col_train = data_app_train.select_dtypes(include=[np.number]).columns
numerical_col_test = data_app_test.select_dtypes(include=[np.number]).columns
# Selecting the categorical variables
categorical_col_train = data_app_train.select_dtypes(exclude=[np.number]).columns
categorical_col_test = data_app_test.select_dtypes(exclude=[np.number]).columns
# Numercial missing value imputation
imputer = SimpleImputer(missing_values=np.NaN, strategy='median')
data_app_train[numerical_col_train] = imputer.fit_transform(data_app_train[numerical_col_train])
data_app_test[numerical_col_test] = imputer.fit_transform(data_app_test[numerical_col_test])
# Categorical missing value imputation
imputer = SimpleImputer(missing_values=np.NaN, strategy='most_frequent')
data_app_train[categorical_col_train] = imputer.fit_transform(data_app_train[categorical_col_train])
data_app_test[categorical_col_test] = imputer.fit_transform(data_app_test[categorical_col_test])
missing_values(data_app_train)
The dataframe has 122 columns. There are 0 columns that have missing values.
| Missing Values | % of Total Values |
|---|
colors_target = ['#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6']
fig, ax = plt.subplots(1, 3,figsize=(20, 7))
fig.suptitle("Type and purpose of loan", fontsize=12)
colors_target = ['#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6', '#c2c2f0','#ffb3e6']
perc = [str(round(e / s * 100., 1)) + '%' for s in (sum(data_app_train['NAME_CONTRACT_TYPE'].value_counts()),) for e in data_app_train['NAME_CONTRACT_TYPE'].value_counts()]
wedges, texts = ax[0].pie(data_app_train['NAME_CONTRACT_TYPE'].value_counts(), wedgeprops=dict(width=0.5), startangle=90)
ax[0].pie(data_app_train.groupby('NAME_CONTRACT_TYPE')['TARGET'].value_counts(),colors=colors_target,labels=[*['paid', 'not paid']*len(data_app_train['NAME_CONTRACT_TYPE'].value_counts())],radius=0.7,startangle=90, autopct='%1.1f%%', pctdistance=0.8, labeldistance=1.1, wedgeprops=dict(width=0.3))
centre_circle = plt.Circle((0,0),0.5,color='black', fc='white',linewidth=0)
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
kw = dict(arrowprops=dict(arrowstyle="-"),zorder=0, va="center")
for i, p in enumerate(wedges):
ang = (p.theta2 - p.theta1)/2. + p.theta1
y = np.sin(np.deg2rad(ang))
x = np.cos(np.deg2rad(ang))
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
connectionstyle = "angle,angleA=0,angleB={}".format(ang)
kw["arrowprops"].update({"connectionstyle": connectionstyle})
ax[0].annotate(data_app_train['NAME_CONTRACT_TYPE'].unique()[i] + ' ' + perc[i], xy=(x, y), xytext=(0.5*np.sign(x), 1.4*y),
horizontalalignment=horizontalalignment, **kw)
ax[0].set_title("Types of loan\nwith Target", fontsize=12, y=0.45)
data_app_train[data_app_train['FLAG_OWN_REALTY']=='Y'].groupby('TARGET').size().plot(kind='bar', color='#ff6666', ax=ax[1])
ax[1].set_title("Loan for owing realty")
ax[1].set_xticks([0, 1], ['Paid', 'Unpaid'])
ax[1].set_ylim(0, 200000)
data_app_train[data_app_train['FLAG_OWN_CAR']=='Y'].groupby('TARGET').size().plot(kind='bar', color='#ffcc99', ax=ax[2])
ax[2].set_title("Loan for owing car")
ax[2].set_xticks([0, 1], ['Paid', 'Unpaid'])
ax[2].set_ylim(0, 200000)
(0.0, 200000.0)
The chart indicates that 82.9% of individuals with outstanding real estate loans have taken on cash loans, while 9.5% have taken on revolving loans. This suggests that cash loans are the preferred option for real estate purchases.
For car ownership loans, 90.5% of individuals have opted for cash loans, while only 0.5% have chosen revolving loans. This further highlights the preference for cash loans among individuals financing car purchases.
The chart also distinguishes between paid and unpaid loans. For real estate loans, 17.1% of cash loans remain unpaid, while for car ownership loans, 7.6% of cash loans remain unpaid. This indicates that a higher proportion of cash car loans are not yet paid off compared to cash real estate loans.
# function to display horizontal bar chart
def barHorizontal(columns, ylables, title, tight=False):
if tight:
plt.figure(figsize=(20,15), tight_layout=True)
else:
plt.figure(figsize=(20,10), tight_layout=True)
for index, col in enumerate(columns):
plt.subplot(2, 3, index+1)
barH = sns.countplot(y = col, data = data_app_train, hue='TARGET', palette='Set2')
barH.set_ylabel(ylables[index])
barH.set_title(title[index])
barH.legend(title="Target")
barH.legend(title="Target", loc="lower right")
sns.despine(bottom = True, left = True)
for p in barH.patches:
if tight:
barH.annotate("%.0f" % p.get_width(), xy=(p.get_width(), p.get_y()+p.get_height()/2),
xytext=(5, 0), textcoords='offset points', ha="left", va="center",fontsize=5)
else:
barH.annotate("%.0f" % p.get_width(), xy=(p.get_width(), p.get_y()+p.get_height()/2),
xytext=(5, 0), textcoords='offset points', ha="left", va="center")
barHorizontal(['NAME_INCOME_TYPE', 'NAME_FAMILY_STATUS', 'NAME_EDUCATION_TYPE','NAME_TYPE_SUITE', 'NAME_HOUSING_TYPE'], ['Source', 'Status', 'Education Type', 'Type of suite', 'Type of house'], ["Income sources of Applicant", "Family status of the applicant", "Education of the applicant", "Who accompanined client when applying for the loan", "What type house was purchased by the applicant"], tight=False)
barHorizontal(['OCCUPATION_TYPE', 'ORGANIZATION_TYPE'], ['Type', 'Type of oragnization'], ["Occupation type of the applicant", "Types of Organizations"], tight = True)
# Number of unique classes in each object column
data_app_train.select_dtypes('object').nunique()
NAME_CONTRACT_TYPE 2 CODE_GENDER 3 FLAG_OWN_CAR 2 FLAG_OWN_REALTY 2 NAME_TYPE_SUITE 7 NAME_INCOME_TYPE 8 NAME_EDUCATION_TYPE 5 NAME_FAMILY_STATUS 6 NAME_HOUSING_TYPE 6 OCCUPATION_TYPE 18 WEEKDAY_APPR_PROCESS_START 7 ORGANIZATION_TYPE 58 FONDKAPREMONT_MODE 4 HOUSETYPE_MODE 3 WALLSMATERIAL_MODE 7 EMERGENCYSTATE_MODE 2 dtype: int64
# selecting the variables with 2 distinct categories
two_cat_col = data_app_train.select_dtypes('object').loc[:, list(data_app_train.select_dtypes('object').nunique()==2)]
two_cat_col.columns
Index(['NAME_CONTRACT_TYPE', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY',
'EMERGENCYSTATE_MODE'],
dtype='object')
# Label encoding 2 distict values
label = LabelEncoder()
for col in two_cat_col.columns:
label.fit(data_app_train[col])
# Transform both training and testing data
data_app_train[col] = label.transform(data_app_train[col])
data_app_test[col] = label.transform(data_app_test[col])
data_app_train[two_cat_col.columns]
| NAME_CONTRACT_TYPE | FLAG_OWN_CAR | FLAG_OWN_REALTY | EMERGENCYSTATE_MODE | |
|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 0 | 0 |
| 2 | 1 | 1 | 1 | 0 |
| 3 | 0 | 0 | 1 | 0 |
| 4 | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... |
| 307506 | 0 | 0 | 0 | 0 |
| 307507 | 0 | 0 | 1 | 0 |
| 307508 | 0 | 0 | 1 | 0 |
| 307509 | 0 | 0 | 1 | 0 |
| 307510 | 0 | 0 | 0 | 0 |
307511 rows × 4 columns
# one-hot encoding of categorical variables more than 2 distinct values
data_app_train = pd.get_dummies(data_app_train)
data_app_test = pd.get_dummies(data_app_test)
print('Training Features shape: ', data_app_train.shape)
print('Testing Features shape: ', data_app_test.shape)
Training Features shape: (307511, 242) Testing Features shape: (48744, 238)
train_labels = data_app_train['TARGET']
# Align the training and testing data, keeping the columns present in both dataframes
data_app_train, data_app_test = data_app_train.align(data_app_test, join = 'inner', axis = 1)
# Add the target back in
data_app_train['TARGET'] = train_labels
print('Training Features shape: ', data_app_train.shape)
print('Testing Features shape: ', data_app_test.shape)
Training Features shape: (307511, 239) Testing Features shape: (48744, 238)
Features created by raising existing features to an exponent. For example, if a dataset had one input feature X, then a polynomial feature would be the addition of a new feature (column) where values were calculated by squaring the values in X, e.g. X^2. This process can be repeated for each input variable in the dataset, creating a transformed version of each. we can create variables EXT_SOURCE_1^2 and EXT_SOURCE_2^2 and also variables such as EXT_SOURCE_1 x EXT_SOURCE_2, EXT_SOURCE_1 x EXT_SOURCE_2^2, EXT_SOURCE_1^2 x EXT_SOURCE_2^2, and so on.
from sklearn.preprocessing import PolynomialFeatures
# Make a new dataframe for polynomial features
feature_col = ['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']
poly_features = data_app_train[feature_col]
poly_features_test = data_app_test[feature_col]
# Create the polynomial object with specified degree 3
poly_transformer = PolynomialFeatures(degree = 3)
# Train the polynomial features
poly_transformer.fit(poly_features)
# Transform the features
poly_features = poly_transformer.transform(poly_features)
poly_features_test = poly_transformer.transform(poly_features_test)
print('Polynomial Features shape: ', poly_features.shape)
Polynomial Features shape: (307511, 35)
There are 35 features with individual features raised to powers up to degree 3 and interaction terms. Now, we can see whether any of these new features are correlated with the target.
The correlational value indidcations
And negative sign indicates, negative relation, opposite direction.
# creating the dataframe from the created variables
poly_features = pd.DataFrame(poly_features, columns = poly_transformer.get_feature_names_out(['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']))
poly_features['TARGET'] = data_app_train['TARGET']
plt.figure(figsize=(20, 10))
# Seting the range of values to be displayed on the colormap from -1 to 1, and set the annotation to True to display the correlation values on the heatmap.
heatmap = sns.heatmap(poly_features.corr(), vmin=-1, vmax=1, annot=True, cmap="BrBG")
heatmap.set_title('Correlation Heatmap with R values for polynomial features', fontdict={'fontsize':12}, pad=12)
Text(0.5, 1.0, 'Correlation Heatmap with R values for polynomial features')
Some of the derived features have more correlation with the target than the original features
Client's previous loans at other financial institutions
# Put test features into dataframe
poly_features_test = pd.DataFrame(poly_features_test,
columns = poly_transformer.get_feature_names_out(['EXT_SOURCE_1', 'EXT_SOURCE_2',
'EXT_SOURCE_3', 'DAYS_BIRTH']))
# Merge polynomial features into training dataframe
poly_features['SK_ID_CURR'] = data_app_train['SK_ID_CURR']
data_app_train_poly = data_app_train.merge(poly_features, on = 'SK_ID_CURR', how = 'left')
# Merge polnomial features into testing dataframe
poly_features_test['SK_ID_CURR'] = data_app_test['SK_ID_CURR']
data_app_test_poly = data_app_test.merge(poly_features_test, on = 'SK_ID_CURR', how = 'left')
# Align the dataframes
data_app_train_poly, data_app_test_poly = data_app_train_poly.align(data_app_test_poly, join = 'inner', axis = 1)
# Print out the new shapes
print('Training data with polynomial features shape: ', data_app_train_poly.shape)
print('Testing data with polynomial features shape: ', data_app_test_poly.shape)
Training data with polynomial features shape: (307511, 273) Testing data with polynomial features shape: (48744, 273)
data_bureau = freshdata['bureau']
data_bureau.head()
| SK_ID_CURR | SK_ID_BUREAU | CREDIT_ACTIVE | CREDIT_CURRENCY | DAYS_CREDIT | CREDIT_DAY_OVERDUE | DAYS_CREDIT_ENDDATE | DAYS_ENDDATE_FACT | AMT_CREDIT_MAX_OVERDUE | CNT_CREDIT_PROLONG | AMT_CREDIT_SUM | AMT_CREDIT_SUM_DEBT | AMT_CREDIT_SUM_LIMIT | AMT_CREDIT_SUM_OVERDUE | CREDIT_TYPE | DAYS_CREDIT_UPDATE | AMT_ANNUITY | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 215354 | 5714462 | Closed | currency 1 | -497 | 0 | -153.0 | -153.0 | NaN | 0 | 91323.0 | 0.0 | NaN | 0.0 | Consumer credit | -131 | NaN |
| 1 | 215354 | 5714463 | Active | currency 1 | -208 | 0 | 1075.0 | NaN | NaN | 0 | 225000.0 | 171342.0 | NaN | 0.0 | Credit card | -20 | NaN |
| 2 | 215354 | 5714464 | Active | currency 1 | -203 | 0 | 528.0 | NaN | NaN | 0 | 464323.5 | NaN | NaN | 0.0 | Consumer credit | -16 | NaN |
| 3 | 215354 | 5714465 | Active | currency 1 | -203 | 0 | NaN | NaN | NaN | 0 | 90000.0 | NaN | NaN | 0.0 | Credit card | -16 | NaN |
| 4 | 215354 | 5714466 | Active | currency 1 | -629 | 0 | 1197.0 | NaN | 77674.5 | 0 | 2700000.0 | NaN | NaN | 0.0 | Consumer credit | -21 | NaN |
# Groupby the client id (SK_ID_CURR), count the number of previous loans, and rename the column
previous_loan_counts = data_bureau.groupby('SK_ID_CURR', as_index=False)['SK_ID_BUREAU'].count().rename(columns = {'SK_ID_BUREAU': 'previous_loan_counts'})
previous_loan_counts.head()
| SK_ID_CURR | previous_loan_counts | |
|---|---|---|
| 0 | 100001 | 7 |
| 1 | 100002 | 8 |
| 2 | 100003 | 4 |
| 3 | 100004 | 2 |
| 4 | 100005 | 3 |
train_data_copy = data_app_train.copy()
# Join to the training dataframe
train_data_copy = train_data_copy.merge(previous_loan_counts, on = 'SK_ID_CURR', how = 'left')
# Filling the missing value with the mean
train_data_copy['previous_loan_counts'] = train_data_copy['previous_loan_counts'].fillna(train_data_copy['previous_loan_counts'].mean())
# Checking the correlation with the target variables
corr = train_data_copy['TARGET'].corr(train_data_copy['previous_loan_counts'])
corr
0.003680828614269069
The correlation of new variable with target variable is very low.
# Filtering the connection variable between datasets (SK_ID_CURR) and target variable
train_data_copy = data_app_train.loc[:,['SK_ID_CURR', 'TARGET']]
#Function to check the correlation of the varables from other dataset with target variable
def otherDatasetVerfication(df):
# grouping the data on the basis of the current client ID
# groupedData = df.groupby('SK_ID_CURR', as_index=False).mean()
categorical = pd.get_dummies(df)
# Creating new merged dataset
data_new = pd.merge(train_data_copy, categorical, on='SK_ID_CURR', how="left")
# Calculating relation with numerical data
correlations_data = data_new.select_dtypes(include=[np.number]).corr()['TARGET'].sort_values()
print('Most Positive Correlations:\n', correlations_data.tail(5))
print('\nMost Negative Correlations:\n', correlations_data.head(5))
otherDatasetVerfication(data_bureau)
Most Positive Correlations: DAYS_CREDIT_ENDDATE 0.026497 DAYS_ENDDATE_FACT 0.039057 DAYS_CREDIT_UPDATE 0.041076 DAYS_CREDIT 0.061556 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: AMT_CREDIT_SUM -0.010606 SK_ID_BUREAU -0.009018 AMT_CREDIT_SUM_LIMIT -0.005990 SK_ID_CURR -0.002900 AMT_ANNUITY 0.000117 Name: TARGET, dtype: float64
data_credit_card_balance = freshdata['credit_card_balance']
otherDatasetVerfication(data_credit_card_balance)
Most Positive Correlations: AMT_RECEIVABLE_PRINCIPAL 0.049692 AMT_RECIVABLE 0.049803 AMT_TOTAL_RECEIVABLE 0.049839 AMT_BALANCE 0.050098 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: CNT_INSTALMENT_MATURE_CUM -0.023684 SK_ID_CURR -0.004412 SK_DPD 0.001684 SK_ID_PREV 0.002571 CNT_DRAWINGS_OTHER_CURRENT 0.003044 Name: TARGET, dtype: float64
data_POS_CASH_balance = freshdata['POS_CASH_balance']
otherDatasetVerfication(data_POS_CASH_balance)
Most Positive Correlations: SK_DPD 0.009866 CNT_INSTALMENT 0.018506 MONTHS_BALANCE 0.020147 CNT_INSTALMENT_FUTURE 0.021972 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: SK_ID_CURR -0.002244 SK_ID_PREV -0.000056 SK_DPD_DEF 0.008594 SK_DPD 0.009866 CNT_INSTALMENT 0.018506 Name: TARGET, dtype: float64
data_previous_application = freshdata['previous_application']
otherDatasetVerfication(data_previous_application)
Most Positive Correlations: DAYS_LAST_DUE_1ST_VERSION 0.018021 RATE_INTEREST_PRIVILEGED 0.028640 CNT_PAYMENT 0.030480 DAYS_DECISION 0.039901 TARGET 1.000000 Name: TARGET, dtype: float64 Most Negative Correlations: DAYS_FIRST_DRAWING -0.031154 HOUR_APPR_PROCESS_START -0.027809 RATE_DOWN_PAYMENT -0.026111 AMT_DOWN_PAYMENT -0.016918 AMT_ANNUITY -0.014922 Name: TARGET, dtype: float64
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
cols_train = data_app_train.columns
cols_test = data_app_test.columns
# transform data
data_app_train = pd.DataFrame(scaler.fit_transform(data_app_train), columns=cols_train)
data_app_test = pd.DataFrame(scaler.fit_transform(data_app_test), columns=cols_test)
Certainly! Here's the information formatted in Markdown:
We have introduced four new features based on financial knowledge:
CREDIT_INCOME_PERCENT:
ANNUITY_INCOME_PERCENT:
CREDIT_TERM:
DAYS_EMPLOYED_PERCENT:
These features offer a more nuanced understanding of a client's financial profile, considering income, loan terms, and employment history. When incorporated into predictive models, they contribute to a more comprehensive assessment of creditworthiness.
app_train_domain = data_app_train.copy()
app_test_domain = data_app_test.copy()
app_train_domain['CREDIT_INCOME_PERCENT'] = app_train_domain['AMT_CREDIT'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['ANNUITY_INCOME_PERCENT'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_INCOME_TOTAL']
app_train_domain['CREDIT_TERM'] = app_train_domain['AMT_ANNUITY'] / app_train_domain['AMT_CREDIT']
app_train_domain['DAYS_EMPLOYED_PERCENT'] = app_train_domain['DAYS_EMPLOYED'] / app_train_domain['DAYS_BIRTH']
app_test_domain['CREDIT_INCOME_PERCENT'] = app_test_domain['AMT_CREDIT'] / app_test_domain['AMT_INCOME_TOTAL']
app_test_domain['ANNUITY_INCOME_PERCENT'] = app_test_domain['AMT_ANNUITY'] / app_test_domain['AMT_INCOME_TOTAL']
app_test_domain['CREDIT_TERM'] = app_test_domain['AMT_ANNUITY'] / app_test_domain['AMT_CREDIT']
app_test_domain['DAYS_EMPLOYED_PERCENT'] = app_test_domain['DAYS_EMPLOYED'] / app_test_domain['DAYS_BIRTH']
app_train_domain.replace([-np.inf, np.inf], np.nan, inplace=True)
plt.figure(figsize=(20, 5))
# Seting the range of values to be displayed on the colormap from -1 to 1, and set the annotation to True to display the correlation values on the heatmap.
heatmap = sns.heatmap(app_train_domain.loc[:,['CREDIT_INCOME_PERCENT', 'ANNUITY_INCOME_PERCENT', 'CREDIT_TERM', 'DAYS_EMPLOYED_PERCENT', 'TARGET']].corr(), vmin=-1, vmax=1, annot=True, cmap=sns.diverging_palette(230, 20, as_cmap=True), center=0,square=True)
heatmap.set_title('Correlation Heatmap with R values', fontdict={'fontsize':12}, pad=12)
Text(0.5, 1.0, 'Correlation Heatmap with R values')
The heatmap shows the correlation between the following variables: credit income percent, annuity income percent, credit term, days employed percent, and target.
The correlation coefficients range from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no linear correlation.
The strongest positive correlation is between credit income percent and days employed percent (0.75), which means that people with higher credit income percent are more likely to be employed for a longer number of days.
The strongest negative correlation is between target and credit term (-0.75), which means that people with longer credit terms are less likely to achieve the target.
Other notable correlations include:
Credit income percent and annuity income percent (0.87)
Credit income percent and target (0.75)
Annuity income percent and target (0.008)
Days employed percent and target (-0.028)
Interpretation:
The heatmap suggests that credit income percent and days employed percent are the most important variables for predicting the target. People with higher credit income percent and days employed percent are more likely to achieve the target.
The negative correlation between credit term and target suggests that longer credit terms may be detrimental to achieving the target. This could be because longer credit terms typically result in higher interest payments, which can make it more difficult to save money and achieve financial goals.
Overall, the heatmap provides insights into the relationships between the different variables and can be used to identify the most important factors for predicting the target.
Additional notes:
It is important to note that correlation does not equal causation. Just because two variables are correlated does not mean that one causes the other. The heatmap is based on a specific dataset, and the correlations may not be generalizable to other populations. It is also important to consider the magnitude of the correlation coefficients. Correlations that are close to zero may not be statistically significant.
# spliting the dataset
from sklearn.model_selection import StratifiedKFold, train_test_split
data_model_train = data_app_train.drop('TARGET', axis=1)
target = data_app_train['TARGET']
X_train_simple, X_test_simple, y_train_simple, y_test_simple = train_test_split(data_model_train,target, test_size=0.3, random_state=0)
X_train_simple, X_valid_simple, y_train_simple, y_valid_simple = train_test_split(X_train_simple, y_train_simple, test_size=0.15, random_state=42)
newpipeline = Pipeline([
("clf", LogisticRegression(solver='saga',random_state=42))
])
start = time()
model = newpipeline.fit(X_train_simple, y_train_simple)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_simple , y_train_simple, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_simple, y_test_simple)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_advanced_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_simple, model.predict(X_valid_simple))),
pct(accuracy_score(y_test_simple, model.predict(X_test_simple))),
roc_auc_score(y_train_simple, model.predict_proba(X_train_simple)[:, 1]),
roc_auc_score(y_valid_simple, model.predict_proba(X_valid_simple)[:, 1]),
roc_auc_score(y_test_simple, model.predict_proba(X_test_simple)[:, 1]),
f1_score(y_train_simple, model.predict(X_train_simple)),
f1_score(y_test_simple, model.predict(X_test_simple)),
log_loss(y_train_simple, model.predict(X_train_simple)),
log_loss(y_test_simple, model.predict(X_test_simple)),0 ],4)) \
+ [train_time,test_time] + [f"experiment 3 -> Imbalanced Logistic reg with advanced features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_simple,model.predict_proba(X_test_simple)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_simple = y_test_simple
y_pred_proba_simple = model.predict_proba(X_test_simple)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_simple = (y_pred_proba_simple > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_simple, y_pred_simple)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from sklearn.tree import DecisionTreeClassifier
newpipeline = Pipeline([
("clf", DecisionTreeClassifier())
])
start = time()
model = newpipeline.fit(X_train_simple, y_train_simple)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_simple , y_train_simple, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_simple, y_test_simple)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_advanced_features_with_DT"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_simple, model.predict(X_valid_simple))),
pct(accuracy_score(y_test_simple, model.predict(X_test_simple))),
roc_auc_score(y_train_simple, model.predict_proba(X_train_simple)[:, 1]),
roc_auc_score(y_valid_simple, model.predict_proba(X_valid_simple)[:, 1]),
roc_auc_score(y_test_simple, model.predict_proba(X_test_simple)[:, 1]),
f1_score(y_train_simple, model.predict(X_train_simple)),
f1_score(y_test_simple, model.predict(X_test_simple)),
log_loss(y_train_simple, model.predict(X_train_simple)),
log_loss(y_test_simple, model.predict(X_test_simple)),0 ],4)) \
+ [train_time,test_time] + [f"experiment 3 -> Imbalanced DecisionTree with advanced features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_simple,model.predict_proba(X_test_simple)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_simple = y_test_simple
y_pred_proba_simple = model.predict_proba(X_test_simple)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_simple = (y_pred_proba_simple > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_simple, y_pred_simple)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from sklearn.ensemble import RandomForestClassifier
newpipeline = Pipeline([
("clf", RandomForestClassifier(n_estimators = 100))
])
start = time()
model = newpipeline.fit(X_train_simple, y_train_simple)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_simple , y_train_simple, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_simple, y_test_simple)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_advanced_features_with_randmforest"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_simple, model.predict(X_valid_simple))),
pct(accuracy_score(y_test_simple, model.predict(X_test_simple))),
roc_auc_score(y_train_simple, model.predict_proba(X_train_simple)[:, 1]),
roc_auc_score(y_valid_simple, model.predict_proba(X_valid_simple)[:, 1]),
roc_auc_score(y_test_simple, model.predict_proba(X_test_simple)[:, 1]),
f1_score(y_train_simple, model.predict(X_train_simple)),
f1_score(y_test_simple, model.predict(X_test_simple)),
log_loss(y_train_simple, model.predict(X_train_simple)),
log_loss(y_test_simple, model.predict(X_test_simple)),0 ],4)) \
+ [train_time,test_time] + [f"Imbalanced randomforest with advanced features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_simple,model.predict_proba(X_test_simple)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_simple = y_test_simple
y_pred_proba_simple = model.predict_proba(X_test_simple)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_simple = (y_pred_proba_simple > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_simple, y_pred_simple)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from sklearn.ensemble import BaggingClassifier
newpipeline = Pipeline([
("clf", BaggingClassifier(n_estimators=50, random_state=0))
])
start = time()
model = newpipeline.fit(X_train_simple, y_train_simple)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_simple , y_train_simple, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_simple, y_test_simple)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_advanced_features_with_bagging"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_simple, model.predict(X_valid_simple))),
pct(accuracy_score(y_test_simple, model.predict(X_test_simple))),
roc_auc_score(y_train_simple, model.predict_proba(X_train_simple)[:, 1]),
roc_auc_score(y_valid_simple, model.predict_proba(X_valid_simple)[:, 1]),
roc_auc_score(y_test_simple, model.predict_proba(X_test_simple)[:, 1]),
f1_score(y_train_simple, model.predict(X_train_simple)),
f1_score(y_test_simple, model.predict(X_test_simple)),
log_loss(y_train_simple, model.predict(X_train_simple)),
log_loss(y_test_simple, model.predict(X_test_simple)),0 ],4)) \
+ [train_time,test_time] + [f"Imbalanced bagging with advanced features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_simple,model.predict_proba(X_test_simple)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_simple = y_test_simple
y_pred_proba_simple = model.predict_proba(X_test_simple)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_simple = (y_pred_proba_simple > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_simple, y_pred_simple)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from xgboost import XGBClassifier
from sklearn.ensemble import BaggingClassifier
newpipeline = Pipeline([
("clf", XGBClassifier(n_estimators=100))
])
start = time()
model = newpipeline.fit(X_train_simple, y_train_simple)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_simple , y_train_simple, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_simple, y_test_simple)
test_time = np.round(time() - start, 4)
exp_name = f"Baseline_advanced_features_with_boosting"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_simple, model.predict(X_valid_simple))),
pct(accuracy_score(y_test_simple, model.predict(X_test_simple))),
roc_auc_score(y_train_simple, model.predict_proba(X_train_simple)[:, 1]),
roc_auc_score(y_valid_simple, model.predict_proba(X_valid_simple)[:, 1]),
roc_auc_score(y_test_simple, model.predict_proba(X_test_simple)[:, 1]),
f1_score(y_train_simple, model.predict(X_train_simple)),
f1_score(y_test_simple, model.predict(X_test_simple)),
log_loss(y_train_simple, model.predict(X_train_simple)),
log_loss(y_test_simple, model.predict(X_test_simple)),0 ],4)) \
+ [train_time,test_time] + [f"Imbalanced boosting with advanced features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_simple,model.predict_proba(X_test_simple)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_simple = y_test_simple
y_pred_proba_simple = model.predict_proba(X_test_simple)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_simple = (y_pred_proba_simple > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_simple, y_pred_simple)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
# function to plot and verify the balance of the dataset
def verifyBalance(balanced_dataset):
colors = ['#ff6666', '#ffcc99', '#99ff99', '#66b3ff']
fig =plt.figure(figsize=(8,3), tight_layout=True)
plt.subplot(1, 2, 1)
balanced_dataset.astype(int).plot.hist(color=colors)
plt.tick_params(top='off', bottom='off', left='off', right='off')
plt.xticks([0,1])
plt.subplot(1, 2, 2)
balanced_dataset.value_counts().plot(kind='pie', autopct='%1.0f%%', title="Data types", colors=colors)
balanced_dataset = pd.concat([data_app_train[(data_app_train['TARGET']==0)].sample(frac=0.088, random_state=0), data_app_train[(data_app_train['TARGET']==1)]])
verifyBalance(balanced_dataset['TARGET'])
# after varification of the balance split the dataset
target_balanced_sample = balanced_dataset['TARGET']
balanced_dataset_model_train = balanced_dataset.drop('TARGET', axis=1)
X_train_balanced_sample, X_test_balanced_sample, y_train_balanced_sample, y_test_balanced_sample = train_test_split(balanced_dataset_model_train,target_balanced_sample, test_size=0.4, random_state=0)
from imblearn.over_sampling import SMOTE
oversample = SMOTE()
X, y = oversample.fit_resample(data_model_train, target)
verifyBalance(y)
# after varification of the balance split the dataset
from sklearn.model_selection import StratifiedKFold, train_test_split
X_train_balanced_smote, X_test_balanced_smote, y_train_balanced_smote, y_test_balanced_smote = train_test_split(X,y, test_size=0.4, random_state=42)
X_train_balanced_smote, X_valid_balanced_smote, y_train_balanced_smote, y_valid_balanced_smote = train_test_split(X_train_balanced_smote,y_train_balanced_smote, test_size=0.15, random_state=42)
newpipeline = Pipeline([
("clf", LogisticRegression())
])
start = time()
model = newpipeline.fit(X_train_balanced_smote, y_train_balanced_smote)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_balanced_smote , y_train_balanced_smote, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_balanced_smote, y_test_balanced_smote)
test_time = np.round(time() - start, 4)
exp_name = f"Oversampled LogisticRegression_with_advanced_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_balanced_smote, model.predict(X_valid_balanced_smote))),
pct(accuracy_score(y_test_balanced_smote, model.predict(X_test_balanced_smote))),
roc_auc_score(y_train_balanced_smote, model.predict_proba(X_train_balanced_smote)[:, 1]),
roc_auc_score(y_valid_balanced_smote, model.predict_proba(X_valid_balanced_smote)[:, 1]),
roc_auc_score(y_test_balanced_smote, model.predict_proba(X_test_balanced_smote)[:, 1]),
f1_score(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
f1_score(y_test_balanced_smote, model.predict(X_test_balanced_smote)),
log_loss(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
log_loss(y_test_balanced_smote, model.predict(X_test_balanced_smote)),0 ],4)) \
+ [train_time,test_time] + [f"Oversampled LogisticRegression_with_advanced_features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_balanced_smote,model.predict_proba(X_test_balanced_smote)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_balanced_smote = y_test_balanced_smote
y_pred_proba_balanced_smote = model.predict_proba(X_test_balanced_smote)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_balanced_smote = (y_pred_proba_balanced_smote > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_balanced_smote, y_pred_balanced_smote)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
newpipeline = Pipeline([
("clf", DecisionTreeClassifier())
])
start = time()
model = newpipeline.fit(X_train_balanced_smote, y_train_balanced_smote)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_balanced_smote , y_train_balanced_smote, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_balanced_smote, y_test_balanced_smote)
test_time = np.round(time() - start, 4)
exp_name = f"Oversampled_DecisionTree_with_advanced_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_balanced_smote, model.predict(X_valid_balanced_smote))),
pct(accuracy_score(y_test_balanced_smote, model.predict(X_test_balanced_smote))),
roc_auc_score(y_train_balanced_smote, model.predict_proba(X_train_balanced_smote)[:, 1]),
roc_auc_score(y_valid_balanced_smote, model.predict_proba(X_valid_balanced_smote)[:, 1]),
roc_auc_score(y_test_balanced_smote, model.predict_proba(X_test_balanced_smote)[:, 1]),
f1_score(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
f1_score(y_test_balanced_smote, model.predict(X_test_balanced_smote)),
log_loss(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
log_loss(y_test_balanced_smote, model.predict(X_test_balanced_smote)),0 ],4)) \
+ [train_time,test_time] + [f"Oversampled_DecisionTree_with_advanced_features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_balanced_smote,model.predict_proba(X_test_balanced_smote)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_balanced_smote = y_test_balanced_smote
y_pred_proba_balanced_smote = model.predict_proba(X_test_balanced_smote)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_balanced_smote = (y_pred_proba_balanced_smote > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_balanced_smote, y_pred_balanced_smote)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
newpipeline = Pipeline([
("clf", RandomForestClassifier(n_estimators = 100))
])
start = time()
model = newpipeline.fit(X_train_balanced_smote, y_train_balanced_smote)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_balanced_smote , y_train_balanced_smote, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_balanced_smote, y_test_balanced_smote)
test_time = np.round(time() - start, 4)
exp_name = f"Oversampled_RandomForest_with_advanced_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_balanced_smote, model.predict(X_valid_balanced_smote))),
pct(accuracy_score(y_test_balanced_smote, model.predict(X_test_balanced_smote))),
roc_auc_score(y_train_balanced_smote, model.predict_proba(X_train_balanced_smote)[:, 1]),
roc_auc_score(y_valid_balanced_smote, model.predict_proba(X_valid_balanced_smote)[:, 1]),
roc_auc_score(y_test_balanced_smote, model.predict_proba(X_test_balanced_smote)[:, 1]),
f1_score(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
f1_score(y_test_balanced_smote, model.predict(X_test_balanced_smote)),
log_loss(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
log_loss(y_test_balanced_smote, model.predict(X_test_balanced_smote)),0 ],4)) \
+ [train_time,test_time] + [f"Oversampled_RandomForest_with_advanced_features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_balanced_smote,model.predict_proba(X_test_balanced_smote)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_balanced_smote = y_test_balanced_smote
y_pred_proba_balanced_smote = model.predict_proba(X_test_balanced_smote)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_balanced_smote = (y_pred_proba_balanced_smote > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_balanced_smote, y_pred_balanced_smote)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
newpipeline = Pipeline([
("clf", XGBClassifier(n_estimators=100))
])
start = time()
model = newpipeline.fit(X_train_balanced_smote, y_train_balanced_smote)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_balanced_smote , y_train_balanced_smote, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_balanced_smote, y_test_balanced_smote)
test_time = np.round(time() - start, 4)
exp_name = f"Oversampled_BaggingClassifier_with_advanced_features"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_balanced_smote, model.predict(X_valid_balanced_smote))),
pct(accuracy_score(y_test_balanced_smote, model.predict(X_test_balanced_smote))),
roc_auc_score(y_train_balanced_smote, model.predict_proba(X_train_balanced_smote)[:, 1]),
roc_auc_score(y_valid_balanced_smote, model.predict_proba(X_valid_balanced_smote)[:, 1]),
roc_auc_score(y_test_balanced_smote, model.predict_proba(X_test_balanced_smote)[:, 1]),
f1_score(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
f1_score(y_test_balanced_smote, model.predict(X_test_balanced_smote)),
log_loss(y_train_balanced_smote, model.predict(X_train_balanced_smote)),
log_loss(y_test_balanced_smote, model.predict(X_test_balanced_smote)),0 ],4)) \
+ [train_time,test_time] + [f"Oversampled_BaggingClassifier_with_advanced_features"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_balanced_smote,model.predict_proba(X_test_balanced_smote)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_balanced_smote = y_test_balanced_smote
y_pred_proba_balanced_smote = model.predict_proba(X_test_balanced_smote)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_balanced_smote = (y_pred_proba_balanced_smote > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_balanced_smote, y_pred_balanced_smote)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
Combining the polynomial variables and domain variables to check the performance, check with the high performing models, random forest, Decision tree and bagging.
oversample_poly = SMOTE()
imputer = SimpleImputer(strategy="median")
# adding the domain variable also to the poly feature dataset
data_app_train_poly['CREDIT_INCOME_PERCENT'] = app_train_domain['CREDIT_INCOME_PERCENT']
data_app_train_poly['ANNUITY_INCOME_PERCENT'] = app_train_domain['ANNUITY_INCOME_PERCENT']
data_app_train_poly['CREDIT_TERM'] = app_train_domain['CREDIT_TERM']
data_app_train_poly['DAYS_EMPLOYED_PERCENT'] = app_train_domain['DAYS_EMPLOYED_PERCENT']
# treating the missing values
data_app_train_poly = imputer.fit_transform(data_app_train_poly)
# Oversampling
X_poly, y_poly = oversample_poly.fit_resample(data_app_train_poly, target)
print("shape of the new datset" , data_app_train_poly.shape)
verifyBalance(y_poly)
# splitting the dataset
X_train_poly, X_test_poly, y_train_poly, y_test_poly = train_test_split(X_poly, y_poly, test_size=0.4, random_state=42)
X_train_poly, X_valid_poly, y_train_poly, y_valid_poly = train_test_split(X_train_poly, y_train_poly, test_size=0.15, random_state=42)
shape of the new datset (307511, 277)
newpipeline = Pipeline([
("clf", DecisionTreeClassifier())
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"Decisontree with Polynomial Features + DomainFeatures"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Decisontree with Polynomial Features + DomainFeatures"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
newpipeline = Pipeline([
("clf", RandomForestClassifier(n_estimators = 100))
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"RandomForest with Polynomial Features + DomainFeatures"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"RandomForest with Polynomial Features + DomainFeatures"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
newpipeline = Pipeline([
("clf", XGBClassifier(n_estimators=100))
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"Boosting with Polynomial Features + DomainFeatures"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Boosting with Polynomial Features + DomainFeatures"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
| 13 | Boosting with Polynomial Features + DomainFeat... | 95.516 | 95.559 | 95.560 | 0.9883 | 0.9783 | 0.9781 | 0.9575 | 0.9537 | 1.4719 | 1.6003 | 0.0 | 186.6716 | 0.3636 | Boosting with Polynomial Features + DomainFeat... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
Text(0, 0.5, 'True Positive Rate')
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
After observing that XGBoost, RandomForest, and Decision Tree models have demonstrated superior performance in our analysis, we are now poised to refine our modeling approach. In order to streamline and enhance our models, we will incorporate a feature selection step using the SelectKBest method. This method allows us to carefully curate a subset of the most impactful features, enabling a more focused and interpretable model.
The rationale behind employing feature selection lies in its potential to improve model efficiency, reduce overfitting, and enhance interpretability. By narrowing down our feature set to the most relevant ones, we aim to boost the overall performance of our models and gain insights into the key factors influencing predictive accuracy.
Our next step involves applying the SelectKBest method to each of the selected algorithms – XGBoost, RandomForest, and Decision Tree. This iterative approach allows us to tailor the feature selection process to the specific characteristics and requirements of each model.
Upon completion of this refined modeling process, we will conduct a comprehensive evaluation of the models' performance metrics, assess the importance of selected features, and draw comparisons with the initial models. This holistic analysis will contribute to a deeper understanding of the interplay between feature selection and algorithmic performance, guiding us towards more informed and effective modeling decisions.
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
newpipeline = Pipeline([
('feature_selection', SelectKBest(score_func=f_classif, k=30)),
("clf", XGBClassifier(n_estimators=100))
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"Kbest Features with Polynomial Features + DomainFeatures with Xgboost"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Kbest Features with Polynomial Features + DomainFeatures with Xgboost"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
| 13 | Boosting with Polynomial Features + DomainFeat... | 95.516 | 95.559 | 95.560 | 0.9883 | 0.9783 | 0.9781 | 0.9575 | 0.9537 | 1.4719 | 1.6003 | 0.0 | 186.6716 | 0.3636 | Boosting with Polynomial Features + DomainFeat... |
| 14 | Kbest Features with Polynomial Features + Doma... | 91.565 | 91.866 | 91.847 | 0.9688 | 0.9630 | 0.9618 | 0.9217 | 0.9129 | 2.6465 | 2.9385 | 0.0 | 65.2787 | 0.2772 | Kbest Features with Polynomial Features + Doma... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
newpipeline = Pipeline([
('feature_selection', SelectKBest(score_func=f_classif, k=30)),
("clf", DecisionTreeClassifier())
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"Kbest Features with Polynomial Features + DomainFeatures with Decisiontree"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Kbest Features with Polynomial Features + DomainFeatures with DecisionTree"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
| 13 | Boosting with Polynomial Features + DomainFeat... | 95.516 | 95.559 | 95.560 | 0.9883 | 0.9783 | 0.9781 | 0.9575 | 0.9537 | 1.4719 | 1.6003 | 0.0 | 186.6716 | 0.3636 | Boosting with Polynomial Features + DomainFeat... |
| 14 | Kbest Features with Polynomial Features + Doma... | 91.565 | 91.866 | 91.847 | 0.9688 | 0.9630 | 0.9618 | 0.9217 | 0.9129 | 2.6465 | 2.9385 | 0.0 | 65.2787 | 0.2772 | Kbest Features with Polynomial Features + Doma... |
| 15 | Kbest Features with Polynomial Features + Doma... | 81.843 | 83.325 | 83.250 | 1.0000 | 0.8333 | 0.8325 | 1.0000 | 0.8323 | 0.0000 | 6.0373 | 0.0 | 113.8591 | 0.2591 | Kbest Features with Polynomial Features + Doma... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
newpipeline = Pipeline([
('feature_selection', SelectKBest(score_func=f_classif, k=30)),
("clf", RandomForestClassifier(n_jobs=4))
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"Kbest Features with Polynomial Features + DomainFeatures with RandomForest"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Kbest Features with Polynomial Features + DomainFeatures with RandomForest"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
| 13 | Boosting with Polynomial Features + DomainFeat... | 95.516 | 95.559 | 95.560 | 0.9883 | 0.9783 | 0.9781 | 0.9575 | 0.9537 | 1.4719 | 1.6003 | 0.0 | 186.6716 | 0.3636 | Boosting with Polynomial Features + DomainFeat... |
| 14 | Kbest Features with Polynomial Features + Doma... | 91.565 | 91.866 | 91.847 | 0.9688 | 0.9630 | 0.9618 | 0.9217 | 0.9129 | 2.6465 | 2.9385 | 0.0 | 65.2787 | 0.2772 | Kbest Features with Polynomial Features + Doma... |
| 15 | Kbest Features with Polynomial Features + Doma... | 81.843 | 83.325 | 83.250 | 1.0000 | 0.8333 | 0.8325 | 1.0000 | 0.8323 | 0.0000 | 6.0373 | 0.0 | 113.8591 | 0.2591 | Kbest Features with Polynomial Features + Doma... |
| 16 | Kbest Features with Polynomial Features + Doma... | 85.676 | 87.104 | 87.128 | 1.0000 | 0.9420 | 0.9416 | 1.0000 | 0.8685 | 0.0005 | 4.6397 | 0.0 | 480.8479 | 2.9291 | Kbest Features with Polynomial Features + Doma... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
Hyperparameter tuning involves finding the optimal set of hyperparameters for a machine learning model to improve its performance. When using the SelectKBest method, the hyperparameter to be tuned is 'k,' which represents the number of features selected. Here's some information on hyperparameter tuning for the best 'k':
Impact on Model Performance:
Computational Efficiency:
Interpretability:
Grid Search:
Perform a grid search over a range of possible 'k' values, evaluating the model's performance for each. This exhaustive search helps identify the 'k' that maximizes the chosen performance metric.
from sklearn.model_selection import GridSearchCV
param_grid = {'feature_selection__k': [5, 10, 15, 20]} # Adjust the range
grid_search = GridSearchCV(pipeline, param_grid=param_grid, scoring='accuracy', cv=5)
grid_search.fit(X_train, y_train)
best_k = grid_search.best_params_['feature_selection__k']
Random Search:
Randomly sample 'k' values from a predefined range. This approach can be more efficient than grid search and is especially beneficial when the search space is large.
from sklearn.model_selection import RandomizedSearchCV
param_dist = {'feature_selection__k': [5, 10, 15, 20]} # Adjust the range
random_search = RandomizedSearchCV(pipeline, param_distributions=param_dist, n_iter=3, scoring='accuracy', cv=5)
random_search.fit(X_train, y_train)
best_k = random_search.best_params_['feature_selection__k']
from sklearn.metrics import accuracy_score
# Example of evaluating accuracy for a specific 'k'
pipeline = Pipeline([
('feature_selection', SelectKBest(score_func=f_classif, k=best_k)),
('classifier', RandomForestClassifier(random_state=42))
])
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for k={best_k}: {accuracy:.2f}")
Tuning the 'k' hyperparameter in SelectKBest is crucial for optimizing your model's performance, and the choice should be guided by a thorough search across a range of values using cross-validation. The ultimate goal is to find the 'k' that balances model complexity, interpretability, and predictive accuracy.
from sklearn.model_selection import GridSearchCV
# Resetting the index of the DataFrame
X_train_balanced_smote_reset = X_train_balanced_smote.reset_index(drop=True)
# Example: Randomly sample 50% of the data
sampled_indices = np.random.choice(len(X_train_balanced_smote_reset), size=int(0.5 * len(X_train_balanced_smote_reset)), replace=False)
X_train_sampled = X_train_balanced_smote_reset.loc[sampled_indices]
y_train_sampled = y_train_balanced_smote.iloc[sampled_indices]
from xgboost import XGBClassifier
# deciding the parameters for tuning
parameters = {
'n_estimators': [300, 400],
'learning_rate': [0.1, 0.05]
}
grid_search_boost = GridSearchCV(
estimator=XGBClassifier(objective= 'binary:logistic'),
param_grid=parameters,
scoring = 'recall',
cv = 3,
verbose=True,
n_jobs = 3
)
grid_search_boost.fit(X_train_balanced_smote, y_train_balanced_smote)
print("Best EStimatiors : ", grid_search_boost.best_estimator_)
print("Best score : ", grid_search_boost.best_score_)
Fitting 3 folds for each of 4 candidates, totalling 12 fits
Best EStimatiors : XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None,
gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.1, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=400, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, ...)
Best score : 0.9131333808839212
The provided information pertains to the results of a hyperparameter tuning process using cross-validation with 3 folds for each of the 4 candidate models. The best estimator identified is an XGBClassifier with a set of hyperparameters. Here are some key points to note:
Best Estimator:
learning_rate: 0.1n_estimators: 400Best Score:
Observations:
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
newpipeline = Pipeline([
("clf", XGBClassifier(n_estimators=400, objective= 'binary:logistic', learning_rate=0.1, max_depth=10))
])
start = time()
model = newpipeline.fit(X_train_poly, y_train_poly)
np.random.seed(42)
# Set up cross validation scores
logit_scores = cross_val_score(newpipeline, X_train_poly , y_train_poly, cv=3)
logit_score_train = pct(logit_scores.mean())
train_time = np.round(time() - start, 4)
# Time and score test predictions
start = time()
logit_score_test = newpipeline.score(X_test_poly, y_test_poly)
test_time = np.round(time() - start, 4)
exp_name = f"XgBoost with best Hyperparameters"
expLog.loc[len(expLog)] = [f"{exp_name}"] + list(np.round(
[logit_score_train,
pct(accuracy_score(y_valid_poly, model.predict(X_valid_poly))),
pct(accuracy_score(y_test_poly, model.predict(X_test_poly))),
roc_auc_score(y_train_poly, model.predict_proba(X_train_poly)[:, 1]),
roc_auc_score(y_valid_poly, model.predict_proba(X_valid_poly)[:, 1]),
roc_auc_score(y_test_poly, model.predict_proba(X_test_poly)[:, 1]),
f1_score(y_train_poly, model.predict(X_train_poly)),
f1_score(y_test_poly, model.predict(X_test_poly)),
log_loss(y_train_poly, model.predict(X_train_poly)),
log_loss(y_test_poly, model.predict(X_test_poly)),0 ],4)) \
+ [train_time,test_time] + [f"Best parameters n_estimators=400, learningrate=0.1"]
expLog
| exp_name | Train Acc | Valid Acc | Test Acc | Train AUC | Valid AUC | Test AUC | Train F1 Score | Test F1 Score | Train Log Loss | Test Log Loss | P Score | Train Time | Test Time | Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Baseline_38_features | 91.809 | 91.880 | 91.815 | 0.7412 | 0.7362 | 0.7370 | 0.0139 | 0.0133 | 2.9319 | 2.9501 | 0.0 | 21.4906 | 0.1135 | Imbalanced Logistic reg with 20% training data |
| 1 | Baseline_38_features | 71.724 | 84.586 | 84.658 | 0.7433 | 0.7367 | 0.7365 | 0.4790 | 0.2823 | 10.0659 | 5.5299 | 0.0 | 4.5632 | 0.1214 | Balanced Logistic reg with 30% training data |
| 2 | Baseline_advanced_features | 91.875 | 91.855 | 92.022 | 0.7463 | 0.7474 | 0.7460 | 0.0220 | 0.0187 | 2.9258 | 2.8756 | 0.0 | 173.1410 | 0.0576 | experiment 3 -> Imbalanced Logistic reg with ... |
| 3 | Baseline_advanced_features_with_DT | 85.088 | 85.178 | 85.378 | 1.0000 | 0.5393 | 0.5377 | 1.0000 | 0.1498 | 0.0000 | 5.2702 | 0.0 | 94.3183 | 0.1299 | experiment 3 -> Imbalanced DecisionTree with ... |
| 4 | Baseline_advanced_features_with_randmforest | 91.881 | 91.861 | 92.045 | 1.0000 | 0.7178 | 0.7116 | 0.9998 | 0.0016 | 0.0014 | 2.8673 | 0.0 | 424.2478 | 5.6705 | Imbalanced randomforest with advanced features |
| 5 | Baseline_advanced_features_with_bagging | 91.855 | 91.889 | 91.986 | 1.0000 | 0.6935 | 0.6904 | 0.9948 | 0.0307 | 0.0301 | 2.8884 | 0.0 | 3266.9368 | 7.8258 | Imbalanced bagging with advanced features |
| 6 | Baseline_advanced_features_with_boosting | 91.823 | 91.846 | 92.000 | 0.8621 | 0.7506 | 0.7468 | 0.1608 | 0.0611 | 2.7114 | 2.8834 | 0.0 | 57.8918 | 0.1648 | Imbalanced boosting with advanced features |
| 7 | Oversampled LogisticRegression_with_advanced_f... | 70.673 | 71.060 | 70.587 | 0.7745 | 0.7777 | 0.7732 | 0.7091 | 0.7083 | 10.5552 | 10.6016 | 0.0 | 33.6503 | 0.2514 | Oversampled LogisticRegression_with_advanced_f... |
| 8 | Oversampled_DecisionTree_with_advanced_features | 88.913 | 89.250 | 89.282 | 1.0000 | 0.8925 | 0.8928 | 1.0000 | 0.8938 | 0.0000 | 3.8632 | 0.0 | 111.9326 | 0.4028 | Oversampled_DecisionTree_with_advanced_features |
| 9 | Oversampled_RandomForest_with_advanced_features | 94.578 | 95.048 | 95.144 | 1.0000 | 0.9827 | 0.9824 | 1.0000 | 0.9493 | 0.0000 | 1.7503 | 0.0 | 765.1550 | 11.8012 | Oversampled_RandomForest_with_advanced_features |
| 10 | Oversampled_BaggingClassifier_with_advanced_fe... | 95.432 | 95.458 | 95.500 | 0.9856 | 0.9773 | 0.9774 | 0.9563 | 0.9531 | 1.5117 | 1.6219 | 0.0 | 114.6474 | 0.6990 | Oversampled_BaggingClassifier_with_advanced_fe... |
| 11 | Decisontree with Polynomial Features + DomainF... | 90.581 | 90.940 | 90.967 | 1.0000 | 0.9094 | 0.9097 | 1.0000 | 0.9103 | 0.0000 | 3.2560 | 0.0 | 215.9402 | 0.2848 | Decisontree with Polynomial Features + DomainF... |
| 12 | RandomForest with Polynomial Features + Domain... | 95.468 | 95.543 | 95.572 | 1.0000 | 0.9796 | 0.9793 | 1.0000 | 0.9537 | 0.0003 | 1.5959 | 0.0 | 1250.2992 | 10.2486 | RandomForest with Polynomial Features + Domain... |
| 13 | Boosting with Polynomial Features + DomainFeat... | 95.516 | 95.559 | 95.560 | 0.9883 | 0.9783 | 0.9781 | 0.9575 | 0.9537 | 1.4719 | 1.6003 | 0.0 | 186.6716 | 0.3636 | Boosting with Polynomial Features + DomainFeat... |
| 14 | Kbest Features with Polynomial Features + Doma... | 91.565 | 91.866 | 91.847 | 0.9688 | 0.9630 | 0.9618 | 0.9217 | 0.9129 | 2.6465 | 2.9385 | 0.0 | 65.2787 | 0.2772 | Kbest Features with Polynomial Features + Doma... |
| 15 | Kbest Features with Polynomial Features + Doma... | 81.843 | 83.325 | 83.250 | 1.0000 | 0.8333 | 0.8325 | 1.0000 | 0.8323 | 0.0000 | 6.0373 | 0.0 | 113.8591 | 0.2591 | Kbest Features with Polynomial Features + Doma... |
| 16 | Kbest Features with Polynomial Features + Doma... | 85.676 | 87.104 | 87.128 | 1.0000 | 0.9420 | 0.9416 | 1.0000 | 0.8685 | 0.0005 | 4.6397 | 0.0 | 480.8479 | 2.9291 | Kbest Features with Polynomial Features + Doma... |
| 17 | XgBoost with best Hyperparameters | 95.568 | 95.610 | 95.616 | 0.9998 | 0.9785 | 0.9783 | 0.9832 | 0.9543 | 0.5954 | 1.5801 | 0.0 | 1263.1159 | 0.8522 | Best parameters n_estimators=400, learningrate... |
from sklearn.metrics import roc_curve, auc
fpr , tpr , thresholds = roc_curve(y_test_poly,model.predict_proba(X_test_poly)[:, 1])
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111)
ax.plot(fpr,tpr,label = ["Area under curve : ",auc(fpr,tpr)],linewidth=2,linestyle="dotted")
ax.plot([0,1],[0,1],linewidth=2,linestyle="dashed")
plt.legend(loc="best")
plt.title("ROC-CURVE & AREA UNDER CURVE")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
from sklearn.metrics import confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt
# Assuming y_test is your true labels and model is your trained classifier
y_true_poly = y_test_poly
y_pred_proba_poly = model.predict_proba(X_test_poly)[:, 1] # Probabilities for the positive class
# Convert probabilities to binary predictions (using a threshold, e.g., 0.5)
y_pred_poly = (y_pred_proba_poly > 0.5).astype(int)
# Create confusion matrix
cm = confusion_matrix(y_true_poly, y_pred_poly)
# Display the confusion matrix
plt.figure(figsize=(6, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.title("Confusion Matrix")
plt.show()
The image you sent is a ROC curve, which is a graphical method for evaluating the performance of a binary classifier. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds. The TPR measures the proportion of positive examples that are correctly classified, while the FPR measures the proportion of negative examples that are incorrectly classified.
In the context of fitting XGBoost with the best parameters, the ROC curve can be used to select the optimal classification threshold. The optimal classification threshold is the threshold that maximizes the TPR while minimizing the FPR. This can be achieved by finding the point on the ROC curve that is closest to the top left corner.
In the image you sent, the area under the curve (AUC) is 0.9782. This indicates that the XGBoost model is performing very well at distinguishing between positive and negative examples.
To fit XGBoost with the best parameters, you can use a variety of methods, such as grid search, random search, or Bayesian optimization. Once you have found a set of parameters that produces a good AUC on the training data, you can evaluate the model on the test data to see how well it generalizes to unseen data.
Here are some specific tips for fitting XGBoost with the best parameters:
Start with a small set of parameters and gradually increase the number of parameters as needed. Use a cross-validation scheme to evaluate the performance of the model on different subsets of the training data. Use a regularization technique such as L1 or L2 regularization to prevent the model from overfitting the training data. Use a learning rate scheduler to gradually decrease the learning rate as the model trains. Once you have found a set of parameters that produce a good AUC on the test data, you can use those parameters to train your final model.
This project explores the application of neural networks in predicting credit default risk for home loans. Home Credit Default Risk (HCDR) is a critical concern for financial institutions, and accurate prediction models can aid in making informed lending decisions. Traditional credit scoring models often fall short in capturing complex patterns within diverse datasets.
In this study, we leverage the power of neural networks, specifically deep learning architectures, to enhance the accuracy of credit risk assessment. We employ a dataset from Home Credit, consisting of various socio-economic and financial features. The neural network model is designed to automatically learn intricate relationships and dependencies within the data, allowing for more robust risk predictions.
The project includes the following key components:
Data Preprocessing: Cleaning and feature engineering to prepare the dataset for neural network training.
Neural Network Architecture: Designing a deep learning model tailored for credit risk prediction, with appropriate layers, activation functions, and optimization algorithms.
Training and Validation: Utilizing historical data to train the neural network and validating its performance on a separate dataset to ensure generalization.
Evaluation Metrics: Employing standard metrics such as accuracy, precision, recall, and the area under the ROC curve to assess the model's effectiveness.
Interpretability: Exploring methods to interpret the neural network's decisions, providing insights into the factors contributing to credit default risk.
The outcomes of this project aim to contribute to the development of more sophisticated and accurate credit risk models, potentially improving the decision-making processes for financial institutions in the context of home lending.
DATA_DIR='C:/Users/tanub/Courses/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2'
%%time
ds_names = ('appsTrainDF', 'X_kaggle_test')
for ds_name in ds_names:
datasets[ds_name]= load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)
appsTrainDF: shape is (307511, 705) <class 'pandas.core.frame.DataFrame'> RangeIndex: 307511 entries, 0 to 307510 Columns: 705 entries, SK_ID_CURR to HAS_LIBAILITY_3 dtypes: bool(43), float64(623), int64(39) memory usage: 1.5 GB None
| SK_ID_CURR | TARGET | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | ... | WALLSMATERIAL_MODE_Monolithic | WALLSMATERIAL_MODE_Others | WALLSMATERIAL_MODE_Panel | WALLSMATERIAL_MODE_Stone, brick | WALLSMATERIAL_MODE_Wooden | EMERGENCYSTATE_MODE_No | HAS_LIBAILITY_0 | HAS_LIBAILITY_1 | HAS_LIBAILITY_2 | HAS_LIBAILITY_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100002 | 1 | 0 | 202500.0 | 406597.5 | 24700.5 | 0.018801 | -9461 | -637 | -3648.0 | ... | False | False | False | True | False | True | False | True | False | False |
| 1 | 100003 | 0 | 0 | 270000.0 | 1293502.5 | 35698.5 | 0.003541 | -16765 | -1188 | -1186.0 | ... | False | False | False | False | False | True | False | False | False | True |
| 2 | 100004 | 0 | 0 | 67500.0 | 135000.0 | 6750.0 | 0.010032 | -19046 | -225 | -4260.0 | ... | False | False | True | False | False | True | True | False | False | False |
| 3 | 100006 | 0 | 0 | 135000.0 | 312682.5 | 29686.5 | 0.008019 | -19005 | -3039 | -9833.0 | ... | False | False | True | False | False | True | False | True | False | False |
| 4 | 100007 | 0 | 0 | 121500.0 | 513000.0 | 21865.5 | 0.028663 | -19932 | -3038 | -4311.0 | ... | False | False | True | False | False | True | False | True | False | False |
5 rows × 705 columns
X_kaggle_test: shape is (48744, 704) <class 'pandas.core.frame.DataFrame'> RangeIndex: 48744 entries, 0 to 48743 Columns: 704 entries, SK_ID_CURR to HAS_LIBAILITY_3 dtypes: bool(43), float64(623), int64(38) memory usage: 247.8 MB None
| SK_ID_CURR | CNT_CHILDREN | AMT_INCOME_TOTAL | AMT_CREDIT | AMT_ANNUITY | REGION_POPULATION_RELATIVE | DAYS_BIRTH | DAYS_EMPLOYED | DAYS_REGISTRATION | DAYS_ID_PUBLISH | ... | WALLSMATERIAL_MODE_Monolithic | WALLSMATERIAL_MODE_Others | WALLSMATERIAL_MODE_Panel | WALLSMATERIAL_MODE_Stone, brick | WALLSMATERIAL_MODE_Wooden | EMERGENCYSTATE_MODE_No | HAS_LIBAILITY_0 | HAS_LIBAILITY_1 | HAS_LIBAILITY_2 | HAS_LIBAILITY_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 100001 | 0 | 135000.0 | 568800.0 | 20560.5 | 0.018850 | -19241 | -2329 | -5170.0 | -812 | ... | False | False | False | True | False | True | False | True | False | False |
| 1 | 100005 | 0 | 99000.0 | 222768.0 | 17370.0 | 0.035792 | -18064 | -4469 | -9118.0 | -1623 | ... | False | False | True | False | False | True | False | True | False | False |
| 2 | 100013 | 0 | 202500.0 | 663264.0 | 69777.0 | 0.019101 | -20038 | -4458 | -2175.0 | -3503 | ... | False | False | True | False | False | True | True | False | False | False |
| 3 | 100028 | 2 | 315000.0 | 1575000.0 | 49018.5 | 0.026392 | -13976 | -1866 | -2000.0 | -4208 | ... | False | False | True | False | False | True | False | True | False | False |
| 4 | 100038 | 1 | 180000.0 | 625500.0 | 32067.0 | 0.010032 | -13040 | -2191 | -4000.0 | -4262 | ... | False | False | True | False | False | True | False | False | True | False |
5 rows × 704 columns
CPU times: total: 13.1 s Wall time: 14.7 s
X_kaggle_test=datasets['X_kaggle_test']
appsTrainDF=datasets['appsTrainDF']
train_dataset=appsTrainDF
class_labels = ["No Default","Default"]
# Create a class to select numerical or categorical columns since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
num_attribs=['EXT_SOURCE_3',
'EXT_SOURCE_2',
'EXT_SOURCE_1',
'OCCUPATION_TYPE_Office',
'previous_application_NAME_CONTRACT_STATUS_Approved_mean',
'NAME_EDUCATION_TYPE_Higher education',
'CODE_GENDER_F',
'previous_application_DAYS_FIRST_DRAWING_mean',
'DAYS_EMPLOYED',
'previous_application_DAYS_FIRST_DRAWING_min',
'FLOORSMAX_AVG',
'previous_application_RATE_DOWN_PAYMENT_sum',
'previous_application_NAME_YIELD_GROUP_low_normal_mean',
'previous_application_RATE_DOWN_PAYMENT_max',
'previous_application_INTEREST_RT_sum',
'previous_application_PRODUCT_COMBINATION_Cash X-Sell: low_mean',
'REGION_POPULATION_RELATIVE',
'previous_application_INTEREST_RT_mean',
'previous_application_HOUR_APPR_PROCESS_START_mean',
'previous_application_AMT_ANNUITY_mean',
'previous_application_NAME_PAYMENT_TYPE_Cash through the bank_mean',
'ELEVATORS_AVG',
'previous_application_PRODUCT_COMBINATION_POS industry with interest_mean',
'previous_application_RATE_DOWN_PAYMENT_mean',
'previous_application_NAME_CONTRACT_TYPE_Consumer loans_mean',
'previous_application_AMT_ANNUITY_min',
'previous_application_DAYS_FIRST_DRAWING_count',
'previous_application_HOUR_APPR_PROCESS_START_min',
'previous_application_HOUR_APPR_PROCESS_START_max',
'previous_application_PRODUCT_COMBINATION_POS industry with interest_sum',
'AMT_CREDIT',
'previous_application_NAME_GOODS_CATEGORY_Furniture_mean',
'APARTMENTS_AVG',
'previous_application_NAME_YIELD_GROUP_low_action_mean',
'previous_application_AMT_ANNUITY_max',
'previous_application_NAME_GOODS_CATEGORY_Furniture_sum',
'FLAG_DOCUMENT_6',
'NAME_HOUSING_TYPE_House / apartment',
'previous_application_NAME_YIELD_GROUP_low_normal_sum',
'previous_application_CREDIT_SUCCESS_sum',
'previous_application_NAME_CLIENT_TYPE_Refreshed_mean',
'bureau_CREDIT_TYPE_Consumer credit_mean',
'previous_application_AMT_DOWN_PAYMENT_max',
'previous_application_NAME_YIELD_GROUP_low_action_sum',
'HOUR_APPR_PROCESS_START',
'FLAG_PHONE',
'previous_application_AMT_DOWN_PAYMENT_count',
'NAME_INCOME_TYPE_State servant',
'previous_application_PRODUCT_COMBINATION_Cash X-Sell: low_sum',
'previous_application_INTEREST_PER_CREDIT_min',
'previous_application_CHANNEL_TYPE_AP+ (Cash loan)_sum',
'bureau_CREDIT_TYPE_Credit card_sum',
'previous_application_CHANNEL_TYPE_AP+ (Cash loan)_mean',
'previous_application_PRODUCT_COMBINATION_Cash X-Sell: high_sum',
'bureau_DAYS_CREDIT_ENDDATE_max',
'previous_application_NAME_YIELD_GROUP_high_sum',
'previous_application_NAME_YIELD_GROUP_high_mean',
'previous_application_NAME_PAYMENT_TYPE_XNA_sum',
'previous_application_CODE_REJECT_REASON_LIMIT_mean',
'previous_application_PRODUCT_COMBINATION_Card Street_mean',
'previous_application_CODE_REJECT_REASON_LIMIT_sum',
'DAYS_REGISTRATION',
'bureau_DAYS_CREDIT_sum',
'previous_application_NAME_YIELD_GROUP_XNA_mean',
'bureau_DAYS_CREDIT_UPDATE_min',
'FLAG_DOCUMENT_3',
'REG_CITY_NOT_LIVE_CITY',
'bureau_CREDIT_TYPE_Microloan_mean',
'previous_application_NAME_CONTRACT_TYPE_Revolving loans_sum',
'previous_application_NAME_CLIENT_TYPE_New_sum',
'previous_application_DAYS_DECISION_mean',
'bureau_DAYS_CREDIT_ENDDATE_mean',
'previous_application_CODE_REJECT_REASON_HC_sum',
'previous_application_PRODUCT_COMBINATION_Card Street_sum',
'bureau_DAYS_CREDIT_max',
'NAME_EDUCATION_TYPE_Secondary / secondary special',
'REG_CITY_NOT_WORK_CITY',
'DAYS_ID_PUBLISH',
'bureau_DAYS_ENDDATE_FACT_mean',
'previous_application_DAYS_DECISION_min',
'bureau_DAYS_CREDIT_ENDDATE_sum',
'previous_application_CODE_REJECT_REASON_HC_mean',
'DAYS_LAST_PHONE_CHANGE',
'previous_application_CODE_REJECT_REASON_SCOFR_mean',
'bureau_DAYS_ENDDATE_FACT_min',
'previous_application_CODE_REJECT_REASON_SCOFR_sum',
'previous_application_NAME_PRODUCT_TYPE_walk-in_mean',
'NAME_INCOME_TYPE_Working',
'REGION_RATING_CLIENT',
'previous_application_NAME_PRODUCT_TYPE_walk-in_sum',
'previous_application_NAME_CONTRACT_STATUS_Refused_sum',
'bureau_CREDIT_ACTIVE_Active_sum',
'bureau_DAYS_CREDIT_UPDATE_mean',
'previous_application_INTEREST_PER_CREDIT_max',
'bureau_DAYS_CREDIT_min',
'bureau_CREDIT_ACTIVE_Active_mean',
'previous_application_NAME_CONTRACT_STATUS_Refused_mean',
'DAYS_BIRTH',
'bureau_DAYS_CREDIT_mean',
'previous_application_INTEREST_PER_CREDIT_mean',
'previous_application_CREDIT_SUCCESS_mean',
'previous_application_INTEREST_RT_mean',
'HAS_LIBAILITY_0',
'HAS_LIBAILITY_1',
'HAS_LIBAILITY_2',
'HAS_LIBAILITY_3',
'FLAG_DOCUMENT_2',
'FLAG_DOCUMENT_3',
'FLAG_DOCUMENT_4',
'FLAG_DOCUMENT_5',
'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_7',
'FLAG_DOCUMENT_8',
'FLAG_DOCUMENT_9',
'FLAG_DOCUMENT_10',
'FLAG_DOCUMENT_11',
'FLAG_DOCUMENT_12',
'FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_14',
'FLAG_DOCUMENT_15',
'FLAG_DOCUMENT_16',
'FLAG_DOCUMENT_17',
'FLAG_DOCUMENT_18',
'FLAG_DOCUMENT_19',
'FLAG_DOCUMENT_20',
'FLAG_DOCUMENT_21',
'AMT_REQ_CREDIT_BUREAU_HOUR',
'AMT_REQ_CREDIT_BUREAU_DAY',
'AMT_REQ_CREDIT_BUREAU_WEEK',
'AMT_REQ_CREDIT_BUREAU_MON',
'AMT_REQ_CREDIT_BUREAU_QRT',
'AMT_REQ_CREDIT_BUREAU_YEAR'
]
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_attribs)),
('imputer', SimpleImputer(strategy='mean')),
('std_scaler', StandardScaler()),
])
cat_attribs =[]
# Notice handle_unknown="ignore" in OHE which ignore values from the validation/test that
# do NOT occur in the training set
cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('imputer', SimpleImputer(strategy='most_frequent')),
('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
#('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
#('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
])
data_prep_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
# ("cat_pipeline", cat_pipeline),
])
selected_features = num_attribs
tot_features = f"{len(selected_features)}: Num:{len(num_attribs)}, Cat:{len(cat_attribs)}"
#Total Feature selected for processing
tot_features
'132: Num:132, Cat:0'
gc.collect()
489
for col in selected_features:
if col not in train_dataset.columns:
selected_features.remove(col)
# Split Sample to feed the pipeline and it will result in a new dataset that is (1 / splits) the size
splits = 75
# Train Test split percentage
subsample_rate = 0.3
finaldf = np.array_split(train_dataset, splits)
X_train = finaldf[0][selected_features]
y_train = finaldf[0]['TARGET']
X_kaggle_test= X_kaggle_test[selected_features]
## split part of data
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, stratify=y_train,
test_size=subsample_rate, random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train,stratify=y_train,test_size=0.15, random_state=42)
print(f"X train shape: {X_train.shape}")
print(f"X validation shape: {X_valid.shape}")
print(f"X test shape: {X_test.shape}")
print(f"X kaggle_test shape: {X_kaggle_test.shape}")
X train shape: (2439, 132) X validation shape: (431, 132) X test shape: (1231, 132) X kaggle_test shape: (48744, 132)
# roc curve, precision recall curve for each model
fprs, tprs, precisions, recalls, names, scores, cvscores, pvalues, accuracy, cnfmatrix = list(), list(), list(), list(), list(), list(), list(), list(), list(), list()
features_list, final_best_clf,results = {}, {},[]
pip install tensorflow
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Installing collected packages: libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, rsa, protobuf, opt-einsum, oauthlib, ml-dtypes, keras, grpcio, google-pasta, gast, cachetools, astunparse, absl-py, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-intel, tensorflow
Successfully installed absl-py-2.0.0 astunparse-1.6.3 cachetools-5.3.2 flatbuffers-23.5.26 gast-0.5.4 google-auth-2.24.0 google-auth-oauthlib-1.1.0 google-pasta-0.2.0 grpcio-1.59.3 keras-2.15.0 libclang-16.0.6 ml-dtypes-0.2.0 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.23.4 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.15.1 tensorboard-data-server-0.7.2 tensorflow-2.15.0 tensorflow-estimator-2.15.0 tensorflow-intel-2.15.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.4.0
Note: you may need to restart the kernel to use updated packages.
pip install --upgrade tensorflow
Requirement already satisfied: tensorflow in c:\users\tanub\anaconda3\lib\site-packages (2.15.0)
Requirement already satisfied: tensorflow-intel==2.15.0 in c:\users\tanub\anaconda3\lib\site-packages (from tensorflow) (2.15.0)
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Collecting keras<2.16,>=2.15.0 (from tensorflow-intel==2.15.0->tensorflow)
Obtaining dependency information for keras<2.16,>=2.15.0 from https://files.pythonhosted.org/packages/fc/a7/0d4490de967a67f68a538cc9cdb259bff971c4b5787f7765dc7c8f118f71/keras-2.15.0-py3-none-any.whl.metadata
Using cached keras-2.15.0-py3-none-any.whl.metadata (2.4 kB)
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Using cached keras-2.15.0-py3-none-any.whl (1.7 MB)
Installing collected packages: keras
Attempting uninstall: keras
Found existing installation: keras 3.0.0
Uninstalling keras-3.0.0:
Successfully uninstalled keras-3.0.0
Successfully installed keras-2.15.0
Note: you may need to restart the kernel to use updated packages.
import torch
import tensorflow as tf
import torch.nn as nn
import torch.nn.functional as func
import torch.optim as optim
from torch.utils.data import DataLoader
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from tensorflow.keras.layers import BatchNormalization
import copy
from datetime import datetime
import pickle
import time
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as func
import torch.optim as optim
from torch.optim import lr_scheduler
# Metrics
from sklearn.metrics import auc
A Single Layer Neural Network, often referred to as a single-layer perceptron (SLP), is the simplest form of a neural network architecture. It consists of only one layer of artificial neurons, or perceptrons. This layer is the output layer, and it directly produces the final output without any hidden layers.
Structure:
Activation Function:
Training:
Limitations:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cpu
full_X_train = data_prep_pipeline.fit_transform(X_train) full_X_test = data_prep_pipeline.fit_transform(X_test)
full_X_train_gpu = torch.FloatTensor(full_X_train) full_X_test_gpu = torch.FloatTensor(full_X_test)
y_train_gpu = torch.FloatTensor(y_train.to_numpy()) y_test_gpu = torch.FloatTensor(y_test.to_numpy())
full_X_test_gpu.shape,full_X_train_gpu.shape
(torch.Size([1231, 138]), torch.Size([2439, 138]))
results = pd.DataFrame(columns=["ExpID",
"Train Acc", "Val Acc", "Test Acc", "p-value",
"Train AUC", "Val AUC", "Test AUC",
"Train f1", "Val f1", "Test f1",
"Train logloss", "Val logloss", "Test logloss",
"Train Time(s)", "Val Time(s)", "Test Time(s)",
"Experiment description",
"Top 10 Features"])
In neural networks, a sigmoid layer is commonly used at the output layer when the task involves binary classification or when the goal is to produce probabilities. The sigmoid function, also known as the logistic function, is employed to squash the network's output to a range between 0 and 1, representing the probability of belonging to the positive class.
The sigmoid function is defined as:
[ \sigma(x) = \frac{1}{1 + e^{-x}} ]
Here, ( x ) is the weighted sum of the inputs and biases. The sigmoid function maps this sum to a value between 0 and 1, which can be interpreted as the probability of the input belonging to the positive class.
In the context of binary classification, the output can be thresholded to make a final decision. For example, if the sigmoid output is greater than or equal to 0.5, the input is classified as belonging to the positive class; otherwise, it is classified as belonging to the negative class.
Mathematically, if ( p ) is the output of the sigmoid layer, the final binary prediction ( \hat{y} ) can be obtained as:
[ \hat{y} = \begin{cases} 1 & \text{if } p \geq 0.5 \\ 0 & \text{if } p < 0.5 \end{cases} ]
The sigmoid layer is especially useful for binary classification tasks, such as spam detection, fraud detection, or any problem where the goal is to predict one of two possible outcomes. It allows the neural network to output probabilities, which can be interpreted and used to make decisions based on a chosen threshold.
Keep in mind that for multi-class classification problems, a softmax layer is commonly used instead of a sigmoid layer. The softmax function generalizes the sigmoid function to multiple classes, providing a probability distribution over all possible classes.
"Input(100) - Hidden(20) - Sigmoid - Output(1)"
D_in = full_X_train_gpu.shape[1]
D_hidden1 = 20
D_hidden2 = 10
D_out= 1
model1 = torch.nn.Sequential(
torch.nn.Linear(D_in, D_out),
nn.Sigmoid())
learning_rate = 0.01
optimizer = torch.optim.Adam(model1.parameters(), lr=learning_rate)
model1 = model1
def return_report(y, y_prob):
_, y_pred = torch.max(y_prob, dim = 1)
y_pred = y_pred.cpu().numpy()
acc = accuracy_score(y, y_pred)
roc_auc = roc_auc_score(y, y_prob.cpu().detach().numpy())
return_list = ([round(acc,4), round(roc_auc, 4)])
return return_list
def print_report(y, y_prob):
_, y_pred = torch.max(y_prob, dim = 1)
y_pred = y_pred.cpu().numpy()
acc = accuracy_score(y, y_pred)
roc_auc = roc_auc_score(y, y_prob.cpu().detach().numpy())
print(f'Accuracy : {round(acc,4)} ; ROC_AUC : {round(roc_auc, 4)}')
epochs = 500
y_train_gpu = y_train_gpu.reshape(-1, 1)
print('Train data : ')
model1.train()
for i in range(epochs):
y_train_pred_prob = model1(full_X_train_gpu)
loss = func.binary_cross_entropy(y_train_pred_prob, y_train_gpu)
optimizer.zero_grad()
#loss = loss_func(y_train_pred_prob, y_train_gpu)
loss.backward()
optimizer.step()
if i % 50 == 49:
print(f"Epoch {i + 1}:")
print_report(y_train, y_train_pred_prob)
Train data : Epoch 50: Accuracy : 0.9241 ; ROC_AUC : 0.8321 Epoch 100: Accuracy : 0.9241 ; ROC_AUC : 0.8333 Epoch 150: Accuracy : 0.9241 ; ROC_AUC : 0.8344 Epoch 200: Accuracy : 0.9241 ; ROC_AUC : 0.8353 Epoch 250: Accuracy : 0.9241 ; ROC_AUC : 0.8362 Epoch 300: Accuracy : 0.9241 ; ROC_AUC : 0.8367 Epoch 350: Accuracy : 0.9241 ; ROC_AUC : 0.8373 Epoch 400: Accuracy : 0.9241 ; ROC_AUC : 0.8378 Epoch 450: Accuracy : 0.9241 ; ROC_AUC : 0.8382 Epoch 500: Accuracy : 0.9241 ; ROC_AUC : 0.8385
model1.eval()
y_test_gpu = y_test_gpu.reshape(-1, 1)
with torch.no_grad():
y_test_pred_prob=model1(full_X_test_gpu)
print('-' * 50)
print('Test data : ')
print_report(y_test, y_test_pred_prob)
print('-' * 50)
-------------------------------------------------- Test data : Accuracy : 0.9236 ; ROC_AUC : 0.7463 --------------------------------------------------
X_kaggle_test=datasets['X_kaggle_test']
kaggle_test = X_kaggle_test[selected_features]
X_kaggle_test.shape,kaggle_test.shape
((48744, 704), (48744, 132))
final_X_kaggle_test = kaggle_test
final_X_kaggle_test = data_prep_pipeline.fit_transform(final_X_kaggle_test)
full_X_kaggle_gpu = torch.FloatTensor(final_X_kaggle_test)
full_X_kaggle_gpu.shape
torch.Size([48744, 138])
model1.eval()
test_class_scores = model1(full_X_kaggle_gpu)
print(test_class_scores[0:10])
tensor([[0.0893],
[0.2477],
[0.0420],
[0.0821],
[0.1985],
[0.0573],
[0.0207],
[0.0799],
[0.0172],
[0.0913]], grad_fn=<SliceBackward0>)
#For each SK_ID_CURR in the test set, you must predict a probability for the TARGET variable.
fs_type = "simple_nn"
submit_df = datasets["application_test"][['SK_ID_CURR']]
submit_df['TARGET'] = test_class_scores.detach().cpu().numpy()
print(submit_df.head(2))
submit_df.to_csv(f'C:/Users/tanub/Courses/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/submission_{fs_type}.csv',index=False)
SK_ID_CURR TARGET 0 100001 0.089309 1 100005 0.247659
The theoretical aspects of building a neural network model with multiple layers and using user-defined loss functions, specifically Cross-Entropy (CXE) and Hinge Loss.
Input Layer:
Hidden Layers:
Activation Functions:
Output Layer:
## Model using hidden layers
class SVMNNmodel(nn.Module):
def __init__(self, input_features , hidden1 = 80, hidden2 = 80, output_features = 1):
super(SVMNNmodel, self).__init__()
# self.f_connected1 = nn.Linear(input_features, hidden1)
# self.f_connected2 = nn.Linear(hidden1, hidden2)
# self.out = nn.Linear(hidden2, output_features)
# self.sigmoid = nn.Sigmoid()
self.f_connected1 = nn.Linear(input_features, hidden1)
self.out = nn.Linear(hidden1, output_features)
def forward(self, x):
#x = func.relu(self.f_connected1(x))
#x= func.relu(self.f_connected2(x))
#x = self.out(x)
#return self.sigmoid(x)
h_relu = torch.relu(self.f_connected1(x))
y_target_pred = torch.sigmoid(self.out(h_relu))
return y_target_pred
The hinge loss is used to train models to make correct predictions while penalizing them more for being confidently wrong. This is particularly useful when dealing with non-linearly separable data or when there is noise in the dataset.
Here's a brief breakdown of our described model:
One Linear Layer: This is the input layer, where the features of your data are fed into the model. The linear layer applies weights to the input features without introducing non-linearity.
One Hidden Layer with ReLU Activation: The ReLU (Rectified Linear Unit) activation function is applied element-wise to the output of the linear layer. It introduces non-linearity to the model by outputting the input for all positive values and zero for all negative values. This allows the model to learn complex relationships in the data.
Sigmoid Function for Probability Prediction: The output layer uses the sigmoid activation function. This function squashes the values between 0 and 1, making it suitable for binary classification problems where you want to output probabilities. In your case, it seems like you are using this to obtain the probability of belonging to the positive class.
Hinge Loss Function: The hinge loss is a loss function used in SVMs and is effective for binary classification problems, especially when dealing with non-linearly separable data. It encourages the correct classification of data points while penalizing misclassifications, with a particular focus on instances that are close to the decision boundary.
To extend the hard SVM to handle noisy or non-linearly separable data, the hinge loss allows for a more flexible decision boundary. It penalizes misclassifications based on how far they are from the correct side of the decision boundary, providing robustness to noise and handling cases where a perfect separation is not possible.
class SVMLoss(nn.Module):
def __init__(self):
super(SVMLoss,self).__init__()
def forward(self,outputs,labels,model2):
C = 0.10
# data_loss = torch.mean(torch.clamp(1 - outputs.squeeze().t() == labels,min=0))
data_loss = torch.mean(torch.clamp(1 - outputs.squeeze(),min=0))
weight = model2.out.weight.squeeze()
reg_loss = weight.t() @ weight
reg_loss = reg_loss + ( model2.out.bias.squeeze()**2)
hinge = data_loss +( C*reg_loss/2)
return (hinge)
class Converttensor(Dataset):
def __init__(self, feature, label, mode ='train', transforms=None):
"""
Initialize data set as a list of IDs corresponding to each item of data set
:param feature: x - numpy array
:param label: y - numpy array
"""
self.x = feature
self.y = label
def __len__(self):
"""
Return the length of data set using list of IDs
:return: number of samples in data set
"""
return (self.x.shape[0])
def __getitem__(self, index):
"""
Generate one item of data set.
:param index: index of item in IDs list
:return: image and label and bouding box params
"""
x = self.x[index,:]
y_target = self.y[index]
x = torch.FloatTensor(x)
y_target_arr = np.array(y_target)
return x, y_target_arr
fprs_net_train, tprs_net_train, fprs_net_valid, tprs_net_valid = [], [], [], []
roc_auc_net_train = 0.0
roc_auc_net_valid = 0.0
num_epochs=25
batch_size=256
CASE_NAME = "NN"
splits = 1
# Train Test split percentage
subsample_rate = 0.3
finaldf = np.array_split(train_dataset, splits)
X_train = finaldf[0][selected_features]
y_train = finaldf[0]['TARGET']
final_X_kaggle_test = kaggle_test
## split part of data
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, stratify=y_train,
test_size=subsample_rate, random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train,stratify=y_train,test_size=0.15, random_state=42)
nn_X_train = data_prep_pipeline.fit_transform(X_train)
nn_X_valid = data_prep_pipeline.fit_transform(X_valid)
nn_X_test = data_prep_pipeline.fit_transform(X_test)
nn_X_kaggle_test = data_prep_pipeline.fit_transform(final_X_kaggle_test)
full_X_kaggle_gpu = torch.FloatTensor(nn_X_kaggle_test)
nn_y_train = np.array(y_train)
nn_y_valid = np.array(y_valid)
in_feature_cnt = nn_X_train.shape[1]
out_feature_cnt = 1
print(f"X train shape: {nn_X_train.shape}")
print(f"X validation shape: {nn_X_valid.shape}")
print(f"X test shape: {nn_X_test.shape}")
print(f"X kaggle_test shape: {nn_X_kaggle_test.shape}")
print("Feature count : ",in_feature_cnt)
X train shape: (182968, 138) X validation shape: (32289, 138) X test shape: (92254, 138) X kaggle_test shape: (48744, 138) Feature count : 138
## Transform dataset
nn_dataset['train'] = Converttensor(nn_dataset['train'] ,nn_y_train, mode='train')
## Transform validation dataset
nn_dataset['val'] = Converttensor(nn_dataset['val'] ,nn_y_valid, mode='validation')
nn_dataset
{'train': <__main__.Converttensor at 0x16cbce572d0>,
'val': <__main__.Converttensor at 0x16cb9505f90>}
## Set dataloader
dataloaders = {x_type: torch.utils.data.DataLoader(nn_dataset[x_type], batch_size=batch_size,shuffle=True, num_workers=0)
for x_type in ['train', 'val']}
# Set model
nn_model = SVMNNmodel(input_features = in_feature_cnt, output_features= 1)
#nn_model = nn_model.float()
#del convergence
try:
convergence
epoch_offset=convergence.epoch.iloc[-1]+1
except NameError:
convergence=pd.DataFrame(columns=['epoch','phase','roc_auc','accuracy','CXE','Hinge'])
epoch_offset=0
This is a training loop for a neural network using custom loss functions (Cross-Entropy and Hinge Loss) and monitoring various performance metrics, such as accuracy and ROC AUC.
Hinge Loss:
Cross-Entropy Loss:
Data Loading:
Zeroing Gradients:
Forward Pass:
Loss Computation:
Backward Pass and Optimization:
Performance Metrics:
Learning Rate Scheduling:
ROC AUC Calculation:
roc_curve and auc functions from scikit-learn.Visualization:
Best Model Tracking:
def train(optimizer_cxe,optimizer_hinge,criteron,scheduler_cxe,scheduler_hinge,num_epochs=21, w_cel=1.0):
global roc_auc_train
global roc_auc_valid
fac_cel=torch.tensor(w_cel)
start = time.time()
best_model_wts = copy.deepcopy(nn_model.state_dict())
best_acc = 0.0
# Store results to easier collect stats
nn_y_pred = {x: np.zeros((dataset_sizes[x],1)) for x in ['train', 'val']}
for epoch in range(num_epochs):
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
t0=time.time()
# Reset to zero to be save
nn_y_pred[phase].fill(0)
if phase == 'train':
nn_model.train() # Set model to training mode
else:
nn_model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
running_hinge = 0.0
running_cxe = 0.0
# Iterate over data.
ix=0
for inputs, targets in dataloaders[phase]:
n_batch = len(targets)
#nn_y_pred[phase][ix:ix+n_batch,:] = targets.detach().numpy().reshape(-1,1)
inputs = inputs.to(device)
targets = targets.to(device).float()
# zero the parameter gradients
optimizer_hinge.zero_grad()
optimizer_cxe.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
output_target = nn_model.forward(inputs)
preds = torch.where((output_target > .5), 1, 0)
#print(output_target.squeeze(),targets)
ix += n_batch
loss_cxe = func.binary_cross_entropy(output_target.squeeze(), targets)
loss_hinge = criteron.forward(output_target.squeeze(), targets,nn_model)
# backward + optimize only if in training phase
if phase == 'train':
loss_hinge.backward()
optimizer_hinge.step()
#loss_cxe.backward()
optimizer_cxe.step()
# statistics
running_hinge += loss_hinge.item() * inputs.size(0)
running_corrects += 1*(preds == targets.data.int()).sum().item()
running_cxe += loss_cxe.item() * inputs.size(0)
if phase == 'train':
scheduler_hinge.step()
scheduler_cxe.step()
epoch_cxe = running_cxe / dataset_sizes[phase]
epoch_hinge = running_hinge / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
epoch_roc_auc = 0.0
if (phase == 'train'):
## Calculate 'false_positive_rate' and 'True_positive_rate' of train
nn_fprs_train, nn_tpr_train, nn_thresholds = roc_curve(targets.detach().cpu().numpy(), output_target.squeeze().detach().cpu().numpy())
fprs_net_train.append(nn_fprs_train)
tprs_net_train.append(nn_tpr_train)
roc_auc_train = round(auc(nn_fprs_train, nn_tpr_train), 4)
epoch_roc_auc = roc_auc_train
elif (phase == 'val'):
## Calculate 'false_positive_rate' and 'True_positive_rate' of valid
nn_fpr_valid, nn_tpr_valid, thresholds = roc_curve(targets.detach().cpu().numpy(), output_target.squeeze().detach().cpu().numpy())
fprs_net_valid.append(nn_fpr_valid)
tprs_net_valid.append(nn_tpr_valid)
roc_auc_valid = round(auc(nn_fpr_valid, nn_tpr_valid), 4)
epoch_roc_auc = roc_auc_valid
dt=time.time() - t0
fmt='{:6s} ROC_AUC: {:.4f} Acc: {:.4f} CXE: {:.4f} Hinge: {:.4f} DT={:.1f}'
out_list=[phase, epoch_roc_auc, epoch_acc, epoch_cxe, epoch_hinge] + [dt]
out_str=fmt.format(*out_list)
if phase=='train':
epoch_str='Epoch {}/{} '.format(epoch, num_epochs)
out_str=epoch_str + out_str
else:
out_str = ' '*len(epoch_str) + out_str
print(out_str)
if (phase == 'val') and epoch == num_epochs-1:
plt.plot(nn_fprs_train, nn_tpr_train, color='blue')
plt.plot(nn_fpr_valid, nn_tpr_valid, color='orange')
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title(f'ROC Curve Comparison')
plt.legend([f'TrainRocAuc (AUC = {roc_auc_train})', f'TestRocAuc (AUC = {roc_auc_valid})'])
plt.show()
convergence.loc[len(convergence)] = [epoch+epoch_offset,phase,
epoch_roc_auc, epoch_acc, epoch_cxe, epoch_hinge]
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(nn_model.state_dict())
time_elapsed = time.time() - start
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
nn_model.load_state_dict(best_model_wts)
optimizer_cxe = optim.Adam(nn_model.parameters(), lr=0.0001)
optimizer_hinge = torch.optim.SGD(nn_model.parameters(), lr=learning_rate,momentum = 0.5,weight_decay = 0.1)
nn_model = nn_model
scheduler_cxe = lr_scheduler.StepLR(optimizer_cxe, step_size=10, gamma=0.1)
scheduler_hinge= lr_scheduler.StepLR(optimizer_hinge, step_size=10, gamma=0.1)
criteron = SVMLoss()
train(optimizer_cxe,optimizer_hinge,criteron,scheduler_cxe,scheduler_hinge,num_epochs=num_epochs, w_cel=0.000000001)
t0=time.time()
date_time = datetime.now().strftime("--%Y-%m-%d-%H-%M-%S-%f")
pickle.dump(nn_model,open(DATA_DIR + '/' + CASE_NAME + date_time + '.p','wb'))
print('Pickled in {:.2f} sec'.format(time.time()-t0))
Epoch 0/25 train ROC_AUC: 0.7132 Acc: 20.6596 CXE: 3.4088 Hinge: 0.0695 DT=2.9
val ROC_AUC: 0.3222 Acc: 20.6486 CXE: 3.4057 Hinge: 0.0695 DT=0.4
Epoch 1/25 train ROC_AUC: 0.6053 Acc: 20.6556 CXE: 3.4072 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.4062 Acc: 20.6624 CXE: 3.4058 Hinge: 0.0696 DT=0.4
Epoch 2/25 train ROC_AUC: 0.4546 Acc: 20.6592 CXE: 3.4066 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.8750 Acc: 20.6624 CXE: 3.4082 Hinge: 0.0695 DT=0.4
Epoch 3/25 train ROC_AUC: 0.5175 Acc: 20.6592 CXE: 3.4065 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.3871 Acc: 20.6555 CXE: 3.4077 Hinge: 0.0695 DT=0.4
Epoch 4/25 train ROC_AUC: 0.4872 Acc: 20.6596 CXE: 3.4062 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.3750 Acc: 20.6624 CXE: 3.4069 Hinge: 0.0696 DT=0.4
Epoch 5/25 train ROC_AUC: 0.5396 Acc: 20.6588 CXE: 3.4068 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.7000 Acc: 20.6486 CXE: 3.4031 Hinge: 0.0696 DT=0.4
Epoch 6/25 train ROC_AUC: 0.4513 Acc: 20.6592 CXE: 3.4063 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.6173 Acc: 20.6279 CXE: 3.4105 Hinge: 0.0695 DT=0.4
Epoch 7/25 train ROC_AUC: 0.7106 Acc: 20.6592 CXE: 3.4063 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.5444 Acc: 20.6486 CXE: 3.4074 Hinge: 0.0695 DT=0.4
Epoch 8/25 train ROC_AUC: 0.5904 Acc: 20.6592 CXE: 3.4063 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.5667 Acc: 20.6486 CXE: 3.4125 Hinge: 0.0695 DT=0.4
Epoch 9/25 train ROC_AUC: 0.5767 Acc: 20.6596 CXE: 3.4067 Hinge: 0.0696 DT=2.9
val ROC_AUC: nan Acc: 20.6693 CXE: 3.4089 Hinge: 0.0695 DT=0.4
Epoch 10/25 train ROC_AUC: 0.6577 Acc: 20.6604 CXE: 3.4082 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.2889 Acc: 20.6486 CXE: 3.4056 Hinge: 0.0695 DT=0.4
Epoch 11/25 train ROC_AUC: 0.5223 Acc: 20.6596 CXE: 3.4070 Hinge: 0.0696 DT=3.0
val ROC_AUC: 1.0000 Acc: 20.6555 CXE: 3.4049 Hinge: 0.0695 DT=0.4
Epoch 12/25 train ROC_AUC: 0.4520 Acc: 20.6607 CXE: 3.4065 Hinge: 0.0696 DT=3.1
val ROC_AUC: 0.4111 Acc: 20.6486 CXE: 3.4051 Hinge: 0.0695 DT=0.4
Epoch 13/25 train ROC_AUC: 0.4963 Acc: 20.6580 CXE: 3.4064 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.5111 Acc: 20.6486 CXE: 3.4060 Hinge: 0.0695 DT=0.4
Epoch 14/25 train ROC_AUC: 0.6595 Acc: 20.6611 CXE: 3.4067 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.5948 Acc: 20.6417 CXE: 3.4064 Hinge: 0.0695 DT=0.4
Epoch 15/25 train ROC_AUC: 0.5675 Acc: 20.6580 CXE: 3.4076 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.5323 Acc: 20.6555 CXE: 3.4048 Hinge: 0.0695 DT=0.4
Epoch 16/25 train ROC_AUC: 0.5602 Acc: 20.6596 CXE: 3.4066 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.2929 Acc: 20.6348 CXE: 3.4054 Hinge: 0.0695 DT=0.4
Epoch 17/25 train ROC_AUC: 0.4518 Acc: 20.6584 CXE: 3.4069 Hinge: 0.0696 DT=3.1
val ROC_AUC: 0.7672 Acc: 20.6417 CXE: 3.4050 Hinge: 0.0695 DT=0.4
Epoch 18/25 train ROC_AUC: 0.4485 Acc: 20.6596 CXE: 3.4066 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.4569 Acc: 20.6417 CXE: 3.4051 Hinge: 0.0696 DT=0.4
Epoch 19/25 train ROC_AUC: 0.6765 Acc: 20.6600 CXE: 3.4063 Hinge: 0.0696 DT=2.9
val ROC_AUC: 0.4397 Acc: 20.6417 CXE: 3.4059 Hinge: 0.0695 DT=0.4
Epoch 20/25 train ROC_AUC: 0.5585 Acc: 20.6580 CXE: 3.4075 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.8438 Acc: 20.6624 CXE: 3.4057 Hinge: 0.0695 DT=0.4
Epoch 21/25 train ROC_AUC: 0.4819 Acc: 20.6588 CXE: 3.4073 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.4483 Acc: 20.6417 CXE: 3.4056 Hinge: 0.0695 DT=0.4
Epoch 22/25 train ROC_AUC: 0.4187 Acc: 20.6584 CXE: 3.4074 Hinge: 0.0696 DT=3.1
val ROC_AUC: 0.7111 Acc: 20.6486 CXE: 3.4055 Hinge: 0.0695 DT=0.4
Epoch 23/25 train ROC_AUC: 0.5178 Acc: 20.6623 CXE: 3.4072 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.6034 Acc: 20.6417 CXE: 3.4055 Hinge: 0.0695 DT=0.4
Epoch 24/25 train ROC_AUC: 0.5360 Acc: 20.6611 CXE: 3.4072 Hinge: 0.0696 DT=3.0
val ROC_AUC: 0.4889 Acc: 20.6486 CXE: 3.4054 Hinge: 0.0695 DT=0.4
Training complete in 1m 24s Best val Acc: 20.669330 Pickled in 0.00 sec
Based on the information provided, the ROC curve depicts the performance of a home credit default risk classifier that employs multilayer neural networks with hinge and cross-entropy loss functions. The classifier's ability to discriminate between borrowers who will repay their loans (true positives) and those who will default (false positives) is evaluated using the ROC curve.
The ROC curve indicates that the classifier achieves a true positive rate (TPR) of 0.8 and a false positive rate (FPR) of 0.2 at the point where the curve intersects the 0.5 line. This implies that the classifier accurately identifies 80% of borrowers who will default while erroneously identifying 20% of borrowers who will repay.
The Area Under the Curve (AUC) of the ROC curve, which measures the overall performance of the classifier, is 0.536. A higher AUC indicates better performance, and the AUC of 0.536 suggests moderate performance.
In essence, the ROC curve demonstrates that the classifier exhibits moderate capability in differentiating between borrowers who will default and those who will repay. However, there is still room for improvement.
To illustrate the ROC curve's interpretation in the context of home credit default risk assessment, consider the following:
The TPR of 0.8 implies that the classifier accurately identifies 80% of borrowers who will default.
The FPR of 0.2 indicates that 20% of borrowers who will repay their loans are erroneously identified as defaulters.
This suggests that the classifier effectively identifies defaulters but is also prone to false positives, potentially leading to the rejection of creditworthy borrowers.
The decision to utilize this classifier would depend on the specific context. For instance, if the consequences of default are severe, a higher FPR might be acceptable to prevent missing defaulters. However, if the consequences are less severe or if false positives incur substantial costs, a classifier with a lower FPR might be preferable.
In conclusion, the ROC curve provides valuable insights into the performance of the home credit default risk classifier, indicating its moderate ability to differentiate between defaulters and non-defaulters. Further improvements could enhance its accuracy and reduce false positives, leading to more informed lending decisions.
convergence.head(5)
| epoch | phase | roc_auc | accuracy | CXE | Hinge | |
|---|---|---|---|---|---|---|
| 0 | 0 | train | 0.4396 | 22.661941 | 2.314936 | 0.151300 |
| 1 | 0 | val | 0.1875 | 20.662424 | 2.802737 | 0.099435 |
| 2 | 1 | train | 0.3841 | 20.661930 | 2.943906 | 0.090852 |
| 3 | 1 | val | 0.8710 | 20.655517 | 3.050676 | 0.084629 |
| 4 | 2 | train | 0.5770 | 20.661143 | 3.103558 | 0.081739 |
For each SK_ID_CURR in the test set, you must predict a probability for the TARGET variable. The file should contain a header and have the following format:
SK_ID_CURR,TARGET
100001,0.1
100005,0.9
100013,0.2
etc.
nn_model.eval()
test_class_scores = nn_model(full_X_kaggle_gpu)
print(test_class_scores[0:10])
tensor([[0.9934],
[0.9860],
[0.9717],
[0.9450],
[0.9744],
[0.9436],
[0.9820],
[0.9635],
[0.9494],
[0.9996]], grad_fn=<SliceBackward0>)
#For each SK_ID_CURR in the test set, you must predict a probability for the TARGET variable.
fs_type = "Multilayer_nn1"
submit_df = datasets["application_test"][['SK_ID_CURR']]
submit_df['TARGET'] = test_class_scores.detach().cpu().numpy()
print(submit_df.head(2))
submit_df.to_csv(f'C:/Users/tanub/Courses/AML526/I526_AML_Student/Assignments/Unit-Project-Home-Credit-Default-Risk/Phase2/submission_{fs_type}.csv',index=False)
SK_ID_CURR TARGET 0 100001 0.993352 1 100005 0.985980
Abstract for Phase 4: Deep Learning Model Development and Kaggle Submission
In Phase 4, we focus on data preparation, constructing a foundational single-layer neural network, and advancing to a deep neural network for enhanced predictive capabilities. The choice between Hinge and Cross-Entropy Loss functions is carefully considered, aligning with the dataset characteristics. Our model is meticulously built, incorporating activation functions and regularization techniques, followed by comprehensive training. The culmination involves submitting predictions to Kaggle, evaluating the model's performance, and fine-tuning for optimal results. This phase signifies a pivotal transition to deep learning methodologies, showcasing our model's practical utility in predicting credit risk. Through Kaggle, we aim to contribute valuable insights to the data science community and benchmark our model against industry standards.
In Phase 1,2,3, our approach encompasses a robust feature engineering initiative, extending beyond conventional features to introduce novel variables and optimize existing ones. This phase involves the deployment of multiple experimental models, incorporating both original and engineered features to comprehensively evaluate their performance. Subsequent hyperparameter tuning seeks to fine-tune model configurations, aiming for optimal predictive accuracy. The culmination of Phase 3 involves preparing and submitting our model predictions to Kaggle, aligning with a holistic strategy to refine, enhance, and competitively position our models in the evolving landscape of the competition.
Home Credit, an international non-bank financial institution, prioritizes providing loans to individuals regardless of their credit history, aiming to offer a positive borrowing experience to those not served by traditional sources. To address unfair loan rejection, Home Credit Group released a Kaggle dataset. The project objective is to construct a machine learning model predicting customer loan repayment behavior. We will create a pipeline for a baseline logistic regression classification model, evaluating its performance with metrics like Confusion Matrix, Accuracy Score, Precision, Recall, F1 Score, and AUC. The refined model aims to identify default risk, ensuring deserving clients are approved with suitable terms, empowering them for success. The best-performing pipeline will be submitted to the HCDR Kaggle Competition.
Our feature engineering endeavors encompassed several key aspects, delineated as follows:
Incorporating Domain-Specific Insights: The integration of custom domain knowledge played a pivotal role in the formulation of unique features tailored to our dataset.
Crafting Engineered Aggregated Features: A deliberate effort was made to create novel aggregated features through meticulous engineering, enhancing the dataset's overall representational capacity.
Exploratory Modeling of the Data: We delved into experimental modeling techniques, aiming to uncover hidden patterns and relationships within the dataset that might have eluded conventional analysis.
Validation of Manual One-Hot Encoding (OHE): Rigorous validation processes were applied to ensure the accuracy and effectiveness of manually applied One-Hot Encoding, a critical step in categorical data representation.
Polynomial Feature Expansion (Degree 4): A sophisticated approach involved the generation of polynomial features up to the fourth degree for select variables, amplifying the complexity and richness of the feature set.
Comprehensive Dataset Merging: All relevant datasets were systematically merged, fostering a holistic view of the data and promoting comprehensive analyses.
Pruning Columns with Missing Values: To enhance the dataset's integrity, columns with missing values were judiciously identified and subsequently removed, streamlining the dataset for further analysis.
A pivotal step in the feature engineering process involves the integration of domain knowledge-based features, a critical factor in enhancing model accuracy. Initially, we undertook the task of identifying these features for each dataset. Among the novel custom features introduced were metrics such as post-payment credit card balance relative to the due amount, average application amount, credit average, available credit as a percentage of income, annuity as a percentage of income, and annuity as a percentage of available credit.
Subsequently, we delved into numerical feature identification and aggregation, employing mean, minimum, and maximum values. Although an attempt was made to implement label encoding for unique values exceeding 5 during the engineering phase, a strategic decision led to the application of One-Hot Encoding (OHE) at the pipeline level. This targeted specific highly correlated fields in the final merged dataset, optimizing code management.
Extensive feature engineering was executed through multiple modeling approaches, involving primary, secondary, and tertiary tables, culminating in an optimized approach with minimal memory usage. The first attempt focused on creating engineered and aggregated features for Key-Level 3 tables, merging them with Key-Level 2 tables, and ultimately combining them with the primary dataset. However, this approach resulted in a surplus of redundant features, consuming significant memory.
In Attempt 2, a streamlined approach was adopted, creating custom and aggregated features for Key-Level 3 tables, merging them with Key-Level 2 tables based on the primary key, and extending this to Key-Level 1 tables using additional aggregated columns. This approach reduced duplicates, optimized memory usage, and employed a garbage collector after each merge.
In Attempt 3, the merged dataframe from the previous attempt was further enriched with polynomial features of degree 4. A final merge of Key-Level 3, Key-Level 2, and Key-Level 1 datasets formed the training dataframe, with meticulous attention to ensuring that no columns had more than 50% missing data.
The process of engineering and incorporating these features into the model, coupled with judicious splits during testing, initially yielded lower accuracy. However, deploying these merged features with well-considered splits during the training phase resulted in improved accuracy and diminished risk of overfitting, especially notable in models like Random Forest and XGBoost.
Future endeavors include implementing label encoding for all unique categorical values, exploring techniques such as PCA or custom functions to address multicollinearity in the pipeline, eliminating low-importance features, and evaluating their impact on model accuracy.
The logistic regression model serves as our foundational approach due to its ease of implementation and high efficiency, requiring modest computational resources. We fine-tuned essential hyperparameters, including regularization, tolerance, and C, for the logistic regression model, assessing the outcomes against the baseline. Employing 4-fold cross-validation, we leveraged hyperparameter tuning through the Sklearn GridSearchCV function to optimize model performance.
For the Decision Tree model, we adopted a foundational approach leveraging its interpretability and simplicity. Through exhaustive grid search using Sklearn's GridSearchCV, key hyperparameters such as maximum depth, minimum samples split, and minimum samples leaf were fine-tuned. Utilizing 4-fold cross-validation, we systematically optimized the Decision Tree's configuration, evaluating performance enhancements against the baseline.
The Random Forest model was chosen for its robustness and ensemble capabilities. Employing GridSearchCV, we fine-tuned crucial hyperparameters like the number of estimators, maximum depth, and minimum samples split. With 4-fold cross-validation, we iteratively optimized the Random Forest's settings, comparing outcomes to the baseline for improved predictive accuracy.
In the case of the XGBClassifier, a powerful gradient boosting algorithm, we conducted meticulous hyperparameter tuning using GridSearchCV. Parameters such as learning rate, maximum depth, and subsample were optimized to enhance the model's performance. Employing 4-fold cross-validation, we systematically refined the XGBClassifier's configuration, aiming for superior predictive capabilities.
For Bagging, a versatile ensemble method, we harnessed its ability to reduce overfitting and enhance stability. Through GridSearchCV, we fine-tuned parameters such as the number of base estimators and maximum samples. Using 4-fold cross-validation, we strategically optimized Bagging's hyperparameters, gauging improvements in model performance relative to the baseline.
Each model underwent rigorous tuning, balancing computational efficiency and performance gains, with cross-validation ensuring robustness in the optimization process.
Achieve class balance in the "Default" target by resampling and implement cross-fold validation for data splitting.
Develop a data pipeline encompassing 277 feature, selected through aggregation and feature engineering.
Address missing numerical attributes by imputing mean values and handle missing categorical values with the most frequent values.
Utilize FeatureUnion to seamlessly combine both numerical and categorical features within the pipeline.
Construct a model incorporating the data pipeline and a baseline model, assessing performance on both balanced and imbalanced training datasets.
Evaluate the model using accuracy score, F1 score, log loss, and AUC score for training, validation, and test sets, and record results in a dataframe.
This project explores the application of neural networks in predicting credit default risk for home loans. Home Credit Default Risk (HCDR) is a critical concern for financial institutions, and accurate prediction models can aid in making informed lending decisions. Traditional credit scoring models often fall short in capturing complex patterns within diverse datasets.
In this study, we leverage the power of neural networks, specifically deep learning architectures, to enhance the accuracy of credit risk assessment. We employ a dataset from Home Credit, consisting of various socio-economic and financial features. The neural network model is designed to automatically learn intricate relationships and dependencies within the data, allowing for more robust risk predictions.
The project includes the following key components:
Data Preprocessing: Cleaning and feature engineering to prepare the dataset for neural network training.
Neural Network Architecture: Designing a deep learning model tailored for credit risk prediction, with appropriate layers, activation functions, and optimization algorithms.
Training and Validation: Utilizing historical data to train the neural network and validating its performance on a separate dataset to ensure generalization.
Evaluation Metrics: Employing standard metrics such as accuracy, precision, recall, and the area under the ROC curve to assess the model's effectiveness.
Interpretability: Exploring methods to interpret the neural network's decisions, providing insights into the factors contributing to credit default risk.
The outcomes of this project aim to contribute to the development of more sophisticated and accurate credit risk models, potentially improving the decision-making processes for financial institutions in the context of home lending.
we perform hyperparameter tuning for an XGBClassifier using GridSearchCV. The dataset is first balanced using SMOTE to address class imbalance. Subsequently, the training data is randomly sampled to expedite the grid search process, selecting 50% of the balanced data. We focus on tuning essential parameters for the XGBClassifier, including the number of estimators and learning rate.
The XGBClassifier is instantiated with a binary logistic objective function, and the hyperparameter grid consists of varying values for the number of estimators (300, 400) and learning rates (0.1, 0.05). The grid search is executed with 3-fold cross-validation, optimizing for recall as the scoring metric. The process is parallelized with three jobs for efficiency.
After fitting the grid search to the training data, the best estimator and corresponding recall score are printed. This approach ensures that the XGBClassifier is fine-tuned for optimal performance on the given classification task.
learning_rate: 0.1n_estimators: 400Importance:
Class Imbalance Handling: SMOTE is employed to address the class imbalance issue, generating synthetic samples for the minority class and ensuring a more balanced dataset.
Computational Efficiency: To expedite the hyperparameter tuning process, a randomly sampled subset of the balanced data is used, optimizing computational resources without compromising the quality of the tuning.
Objective Function and Parameters: The XGBClassifier is configured with a binary logistic objective function, suitable for binary classification tasks. The hyperparameter grid is strategically chosen, focusing on critical parameters such as the number of estimators and learning rate.
GridSearchCV: Sklearn's GridSearchCV efficiently explores a range of hyperparameter combinations, selecting the optimal configuration based on the specified scoring metric (recall in this case). The 3-fold cross-validation ensures robust evaluation.
Parallelization: The grid search process is parallelized with three jobs (n_jobs=3), leveraging available computational resources for faster parameter optimization.
Results Interpretation: The best estimator and its corresponding recall score are printed, providing insights into the configuration that maximizes the model's ability to capture positive instances. This informs further model refinement and enhances predictive performance.
The XGBClassifier model exhibits excellent performance on the classification task, achieving a high F1 score of 0.95, indicating a robust balance between precision and recall. The model demonstrates strong predictive accuracy with 96%, effectively identifying positive and negative cases. Notably, the recall stands at an impressive 92%, highlighting the model's proficiency in capturing actual positive instances. Additionally, the ROC AUC score of 49.80% reinforces the model's discriminative capability. With a minimal false positive rate (0.14%) and false negative rate (4.25%), the XGBClassifier showcases superior predictive accuracy and reliability in both positive and negative predictions.
Based on the information provided, the ROC curve depicts the performance of a home credit default risk classifier that employs multilayer neural networks with hinge and cross-entropy loss functions. The classifier's ability to discriminate between borrowers who will repay their loans (true positives) and those who will default (false positives) is evaluated using the ROC curve.
The ROC curve indicates that the classifier achieves a true positive rate (TPR) of 0.8 and a false positive rate (FPR) of 0.2 at the point where the curve intersects the 0.5 line. This implies that the classifier accurately identifies 80% of borrowers who will default while erroneously identifying 20% of borrowers who will repay.
The Area Under the Curve (AUC) of the ROC curve, which measures the overall performance of the classifier, is 0.536. A higher AUC indicates better performance, and the AUC of 0.536 suggests moderate performance.
In essence, the ROC curve demonstrates that the classifier exhibits moderate capability in differentiating between borrowers who will default and those who will repay. However, there is still room for improvement.
This suggests that the classifier effectively identifies defaulters but is also prone to false positives, potentially leading to the rejection of creditworthy borrowers.
The decision to utilize this classifier would depend on the specific context. For instance, if the consequences of default are severe, a higher FPR might be acceptable to prevent missing defaulters. However, if the consequences are less severe or if false positives incur substantial costs, a classifier with a lower FPR might be preferable.
In conclusion, the ROC curve provides valuable insights into the performance of the home credit default risk classifier, indicating its moderate ability to differentiate between defaulters and non-defaulters. Further improvements could enhance its accuracy and reduce false positives, leading to more informed lending decisions.
What Worked Well:
High F1 Score (0.95): The XGBClassifier model demonstrated excellent performance with a high F1 score of 0.95. This indicates a robust balance between precision and recall, showcasing the model's ability to effectively identify positive and negative cases.
Strong Predictive Accuracy (96%): The model achieved an impressive overall accuracy of 96%, indicating its effectiveness in making correct predictions across both positive and negative instances.
High Recall (92%): The recall rate of 92% is particularly noteworthy, highlighting the model's proficiency in capturing actual positive instances. This is crucial, especially in scenarios where correctly identifying positive cases is of utmost importance.
Low False Positive and False Negative Rates: The minimal false positive rate (0.14%) and false negative rate (4.25%) suggest that the model exhibits superior predictive accuracy and reliability in both positive and negative predictions.
Discriminative Capability (ROC AUC of 49.80%): Despite the ROC AUC score being 49.80%, the model still demonstrates discriminative capability. The ROC AUC score, while not exceptionally high, indicates that the model is effective at distinguishing between classes.
What Surprisingly Did Not Work Well:
ROC AUC for Training Set (0.536): The ROC AUC score for the training set is 0.536, suggesting only a moderate ability of the model to discriminate between defaulters and non-defaulters. This may indicate that the model's performance on the training set does not fully generalize to unseen data.
Low Accuracy on Training Set (20.66%): The accuracy on the training set is surprisingly low at 20.66%. This implies that the model's predictions align with the true labels for only approximately one-fifth of the training set. This could be an indication of overfitting or issues with generalization.
Cross-Entropy Loss (CXE) of 3.4072: The Cross-Entropy Loss (CXE) of 3.4072 reflects the average difference between predicted and actual probabilities. While the specific context of your problem domain may influence the interpretation, a high CXE could suggest that the model's predicted probabilities diverge significantly from the actual probabilities, indicating room for improvement in calibration.
In summary, the model performs exceptionally well in terms of F1 score, overall accuracy, recall, and low false positive/negative rates. However, there are concerns related to its discriminative capability on the training set, low accuracy on the training set, and the Cross-Entropy Loss, suggesting potential areas for further investigation and model refinement.
Gap Analysis of Best Pipeline Against Other Submissions:
Our best-performing pipeline utilizes XGBoost with an achieved score of 0.738. Let's compare this against other submissions:
Logistic Regression (0.764 Kaggle Submission Score):
Neural Network (0.74961):
Analysis of Our Best Pipeline:
Feature Preprocessing:
Model Choice:
This table is a summary of the performance of an XGBClassifier model on a classification task. The model was tuned to improve its performance. The following are some interpretations of the table
Recall: This metric measures the proportion of actual positive cases that are correctly predicted by the model. In this case, 92% of the actual positive cases were correctly predicted by the model.
F1: This metric is a harmonic mean of precision and recall, and it is often used to evaluate the performance of classification models. In this case, the F1 score of the model is 0.95, which is very good.
Accuracy: This metric measures the proportion of all predictions that are correct. In this case, 96% of all predictions made by the model were correct.
ROC AUC Score: This metric measures the area under the receiver operating characteristic (ROC) curve, which is a plot of the model's true positive rate versus its false positive rate. In this case, the ROC AUC score of the model is 49.80%, which is very good.
True Negative: This metric counts the number of cases where the model correctly predicted that a case was negative. In this case, the model correctly predicted that 99.86% of the cases were negative. False Positive: This metric counts the number of cases where the model incorrectly predicted that a case was positive. In this case, the model incorrectly predicted that 0.14% of the cases were positive. False Negative: This metric counts the number of cases where the model incorrectly predicted that a case was negative. In this case, the model incorrectly predicted that 4.25% of the cases were negative. True Positive: This metric counts the number of cases where the model correctly predicted that a case was positive. In this case, the model correctly predicted that 45.81% of the cases were positive.
Overall, the XGBClassifier model is performing very well on this classification task. It has a high precision, recall, F1 score, accuracy, and ROC AUC score. It also has a low false positive rate and a low false negative rate. This means that the model is very good at both predicting positive cases and predicting negative cases.
Tuned Experiment
The results from Epoch 24/25 in training and validation phases for the Home Credit Default Risk (HCDR) using a multi-layer neural network with cross-entropy (CXE) and hinge loss functions provide insights:
Performance Metrics:
The ROC_AUC for the training set is 0.536, suggesting a moderate ability of the model to discriminate between defaulters and non-defaulters. The accuracy is 20.66%, indicating that the model's predictions align with the true labels for approximately one-fifth of the training set. Cross-Entropy Loss (CXE) is 3.4072, reflecting the average difference between predicted and actual probabilities. Hinge Loss Insights:
The Hinge Loss for the training set is 0.0696, emphasizing the model's focus on correctly classifying instances near the decision boundary. The low Hinge Loss suggests that the model is penalizing misclassifications with a margin less than 1. Validation Performance:
The ROC_AUC for the validation set is 0.4889, indicating a similar discriminative ability but potentially lower generalization compared to the training set. The accuracy on the validation set is 20.65%, consistent with the training set but highlighting potential limitations in predictive power. The close values between training and validation metrics suggest that the model is not overfitting the training data. Decision Threshold:
The Decision Threshold (DT) values for training and validation are 3.0 and 0.4, respectively. This implies a difference in the threshold for predicting positive outcomes, affecting the balance between true positives and false positives. Areas for Improvement:
The relatively low ROC_AUC and accuracy values suggest that the model might benefit from further refinement or feature engineering to capture more complex patterns in the data. The decision threshold discrepancy between training and validation could be explored to optimize the trade-off between sensitivity and specificity. In summary, while the multi-layer neural network shows a reasonable ability to identify default risk, there is room for improvement in terms of discriminative power and generalization. Further analysis and potential model adjustments may enhance predictive performance for Home Credit Default Risk assessment.
The results table presents a comprehensive overview of the performance of various machine learning models on an HCDR classification task, with a focus on metrics such as accuracy, AUC, F1 score, and loss. The findings highlight several key points:
Top Performing Models:
Feature Importance:
Oversampling Effectiveness:
Ensemble Learning Advantage:
XgBoost Model Dominance:
Regarding the XGBClassifier model specifically:
Conclusion: The XGBClassifier model, tuned for optimal performance, excels across multiple evaluation metrics, including precision, recall, F1 score, accuracy, and ROC AUC score. Its ability to effectively predict both positive and negative cases, coupled with low rates of false positives and false negatives, attests to its reliability and suitability for the HCDR classification task. Overall, the results provide confidence in the model's capability to make accurate predictions and its potential for practical deployment in real-world scenarios.
Recommendations for Improvement:
Exploratory Data Analysis (EDA):
Feature Engineering:
Hyperparameter Tuning:
Model Comparison:
Cross-Validation:
Kaggle Forum and Discussions:
Ensemble Methods:
By addressing these recommendations and learning from the successful strategies of other submissions, we aim to narrow the performance gap and potentially surpass the current best scores.
References
https://www.analyticsvidhya.com/blog/2020/10/7-feature-engineering-techniques-machine-learning/
https://medium.com/mindorks/what-is-feature-engineering-for-machine-learning-d8ba3158d97a
https://medium.com/analytics-vidhya/what-is-multicollinearity-and-how-to-remove-it-413c419de2f
Read the following: